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What is speech synthesis?

How does speech synthesis work.

Artwork: Context matters: A speech synthesizer needs some understanding of what it's reading.

Artwork: Concatenative versus formant speech synthesis. Left: A concatenative synthesizer builds up speech from pre-stored fragments; the words it speaks are limited rearrangements of those sounds. Right: Like a music synthesizer, a formant synthesizer uses frequency generators to generate any kind of sound.

Articulatory

What are speech synthesizers used for.

Photo: Will humans still speak to one another in the future? All sorts of public announcements are now made by recorded or synthesized computer-controlled voices, but there are plenty of areas where even the smartest machines would fear to tread. Imagine a computer trying to commentate on a fast-moving sports event, such as a rodeo, for example. Even if it could watch and correctly interpret the action, and even if it had all the right words to speak, could it really convey the right kind of emotion? Photo by Carol M. Highsmith, courtesy of Gates Frontiers Fund Wyoming Collection within the Carol M. Highsmith Archive, Library of Congress , Prints and Photographs Division.

Who invented speech synthesis?

Artwork: Speak & Spell—An iconic, electronic toy from Texas Instruments that introduced a whole generation of children to speech synthesis in the late 1970s. It was built around the TI TMC0281 chip.

Anna (c. ~2005)

Olivia (c. ~2020).

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Current research, notes and references ↑    pre-processing in described in more detail in "chapter 7: speech synthesis from textual or conceptual input" of speech synthesis and recognition by wendy holmes, taylor & francis, 2002, p.93ff. ↑    for more on concatenative synthesis, see chapter 14 ("synthesis by concatenation and signal-process modification") of text-to-speech synthesis by paul taylor. cambridge university press, 2009, p.412ff. ↑    for a much more detailed explanation of the difference between formant, concatenative, and articulatory synthesis, see chapter 2 ("low-lever synthesizers: current status") of developments in speech synthesis by mark tatham, katherine morton, wiley, 2005, p.23–37. please do not copy our articles onto blogs and other websites articles from this website are registered at the us copyright office. copying or otherwise using registered works without permission, removing this or other copyright notices, and/or infringing related rights could make you liable to severe civil or criminal penalties. text copyright © chris woodford 2011, 2021. all rights reserved. full copyright notice and terms of use . follow us, rate this page, tell your friends, cite this page, more to explore on our website....

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Introduced in 2016, WaveNet was one of the first AI models to generate natural-sounding speech. Since then, it has inspired research, products, and applications in Google — and beyond.

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The challenge

Learning from human speech, rapid advances, the power of voice, widespread legacy.

For decades, computer scientists tried reproducing nuances of the human voice to make computer-generated voices more natural.

Most text-to-speech systems relied on “concatenative synthesis” — a pain-staking process of cutting voice recordings into phonetic sounds and recombining them to form new words and sentences - or DSP (digital signal processing) algorithms known as "vocoders".

The resulting voices often sounded mechanical and contained artifacts such as glitches, buzzes and whistles. Making changes required entirely new recordings — an expensive and time-consuming process.

WaveNet took a different approach to audio generation by using a neural network to model predict individual audio samples. This approach allowed WaveNet to produce high-fidelity, synthetic audio, allowing people to interact more naturally with their digital products

WaveNet rapidly went from a research prototype to an advanced product used by millions around the world.

Koray Kavukcuoglu Vice President of Research

WaveNet is a generative model trained on human speech samples. It creates waveforms of speech patterns by predicting which sounds are most likely to follow each other, each built one sample at a time, with up to 24,000 samples per second of sound.

The model incorporates natural-sounding elements, such as lip-smacking and breathing patterns. And includes vital layers of communication like intonation, accents, emotion — delivering a richness and depth to computer-generated voices.

For example, when we first introduced WaveNet, we created American English and Mandarin Chinese voices that narrowed the gap between human and computer-generated voices by 50%.

WaveNet is a general purpose technology that has allowed us to unlock a range of new applications, from improving video calls on even the weakest connections to helping people regain their original voice after losing the ability to speak.

Zachary Gleicher Product Manager

Early versions of WaveNet were time consuming to interact with, taking hours to generate just one second of audio.

Using a technique called distillation — transferring knowledge from a larger to smaller model — we reengineered WaveNet to run 1,000 times faster than our research prototype, creating one second of speech in just 50 milliseconds.

In parallel, we also developed WaveRNN — a simpler, faster, and more computationally efficient model that could run on devices, like mobile phones, rather than in a data center.

Both WaveNet and WaveRNN became crucial components of many of Google’s best known services such as the Google Assistant, Maps Navigation, Voice Search and Cloud Text-To-Speech.

They also helped inspire entirely new product experiences. For example, an extension known as WaveNetEQ helped improve the quality of calls for Duo, Google’s video-calling app.

But perhaps one of its most profound impacts was helping people living with progressive neurological diseases like ALS (amyotrophic lateral sclerosis) regain their voice.

In 2014, former NFL linebacker Tim Shaw’s voice deteriorated due to his ALS. To help, Google’s Project Euphonia (developed a service to better understand Shaw’s impaired speech.

WaveRNN was combined with other speech technologies and a dataset of archive media interviews to create a natural-sounding version of Shaw’s voice, helping him speak again.

WaveNet demonstrated an entirely new approach to voice synthesis that helped people regain their voices, translate content across multiple languages, create custom audio content, and much more.

Its emergence also unlocked new research approaches and technologies for generating natural sounding voices.

Today, thanks to WaveNet, there is a new generation of voice synthesis products that continue its legacy and help billions of people around the world overcome barriers in communication, culture, and commerce.

Speech Synthesis: Text-To-Speech Conversion and Artificial Voices

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The artificial generation of speech has fascinated mankind since ancient times. The robotic-sounding artificial voices from the last century are nowadays replaced with more naturally sounding voices based on pre-recorded human speech. Significant progress in data processing led to qualitative leaps in intelligibility and naturalness. Apart from sizable data of the voice donor, a fully fledged text-to-speech (TTS) synthesizer requires further linguistic resources and components of natural language processing including dictionaries with information on pronunciation and word prosody, morphological structure, and parts-of-speech but also procedures for automatic chunking texts in smaller parts, or morpho-syntactic parsing. TTS technology can be used in many different application domains, for instance, as a communicative aid for those who cannot speak and those who cannot see and in situations characterized as “hands busy, eyes busy” often as a part of spoken dialog systems. One remaining big challenge is evaluation of the quality of synthetic speech output and its appropriateness for the needs of the user. There are also promising developments in speech synthesis that go beyond the pure acoustic channel. Multimodal synthesis includes the visual channel, e.g., in talking heads, whereas silent-speech interfaces and brain-to-speech conversion convert articulatory gestures and brain waves, respectively, to spoken output. Although there has been much progress in quality in the last decade, often achieved by processing enormous amounts of data, TTS today is available only for relatively few languages (probably fewer than 50 with a dominance of English). Thus, a major task will be to find or create linguistic resources and make them available for more languages and language varieties.

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Trouvain, J., Möbius, B. (2019). Speech Synthesis: Text-To-Speech Conversion and Artificial Voices. In: Brunn, S., Kehrein, R. (eds) Handbook of the Changing World Language Map. Springer, Cham. https://doi.org/10.1007/978-3-319-73400-2_168-1

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For a pre-made voice, you can use our extensive library of voices. Or, you can clone, customize and fine-tune voices.

Enter the text you want to convert to speech.

Write naturally in any of our supported languages. Our AI will understand the language and context.

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The placement of commas, periods, and other punctuation significantly influences the delivery and pauses in the output.

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Match your content to the ideal speaker. Different profiles have distinct delivery styles, catering to various tones and emotions.

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Refine your output by adjusting voice settings. Find the perfect balance to enhance clarity and authenticity.

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Text to speech has a vast array of applications, some are well established but more are emerging all the time. TTS is ideal for creating explainer videos, converting books into audio and producing creative video content without hiring voice actors. Our speech technology is ideal for any situation where accessibility and engagement can be improved through communicated written content in a high-quality voice.

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How many languages does ElevenLabs support?

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What Is Speech Synthesis And How Does It Work?

Curious about what is speech synthesis? Discover how this technology works and its various applications in this informative guide.

Unreal Speech

Speech synthesis is the artificial production of human speech. This technology enables users to convert written text into spoken words. Text to speech technology can be a valuable tool for individuals with disabilities, language learners, educators, and more. In this blog, we will delve into the world of speech synthesis, exploring how it works, its applications, and its impact on various industries. Let's dive in and discover What is speech synthesis and how it is shaping the future of communication.

Table of Contents

What is speech synthesis, how does speech synthesis work, different approaches and techniques speech synthesizers use to produce audio waveforms, applications and use cases of speech synthesis, 7 best text to speech synthesizers on the market.

Text Analysis

This initial step involves contextual assimilation of the typed text. The software analyzes the text input to understand its context, including recognizing individual words, punctuation, and grammar. Text analysis helps the software generate accurate speech that reflects the intended meaning of the written content.

Linguistic Processing

Linguistic processing involves mapping the text to its corresponding unit of sound. This process helps convert the written words into phonetic sounds used to develop the spoken language. Linguistic processing ensures that the synthesized speech sounds natural and understandable to the listener.

Acoustic Processing

Acoustic processing plays a crucial role in generating the speech's sound qualities, such as pitch, intensity, and tempo. This step focuses on converting the linguistic representations into acoustic signals that mimic the qualities of human speech. Acoustic processing enhances the naturalness of the synthesized speech .

Audio Synthesis

The final step in the speech synthesis process involves the conversion of the generated sound in the textual sequence using synthetic voices or recorded human voices. Audio synthesis aims to create a realistic speech output that closely resembles human speech. This stage ensures that the synthesized speech is clear, coherent, and engaging for the listener.

Affordable Text-to-Speech Solution

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price.

Text Input and Analysis

After entering the text you want to convert into speech, the TTS software analyzes the text to understand its linguistic components, breaking it down into phonemes, the smallest units of sound in a language. It then identifies punctuation, emphasis, and other cues to generate natural-sounding speech.

In this stage, the software applies rules of grammar and syntax to ensure that the speech sounds natural. It also incorporates intonation and prosody to convey meaning and emotion, enhancing the naturalness of the synthesized speech.

Linguistic information is converted into parameters governing speech sound generation, transforming linguistic features like phonemes and intonation into acoustic parameters. Pitch, duration, and amplitude are manipulated to produce speech sounds with the desired characteristics.

Acoustic parameters are combined to generate audible speech, possibly undergoing filtering and post-processing to enhance clarity and realism.

Accessible Text-to-Speech Technology

If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into enhanced clarity at an affordable and scalable price.

Concatenative Synthesis

Concatenative synthesis involves piecing together pre-recorded segments of speech to create the desired output. It relies on a database of recorded speech units, such as phonemes, syllables, or words, which are concatenated to form complete utterances. This approach can produce highly natural-sounding speech especially when the database contains a large variety of speech units.

Parametric Synthesis

Parametric synthesis generates speech signals by manipulating a set of acoustic parameters that represent various aspects of speech production. These parameters typically include fundamental frequency (pitch), formant frequencies, duration, and intensity. Rather than relying on recorded speech samples, parametric synthesis algorithms use mathematical models to generate speech sounds based on these parameters.

Articulatory Synthesis

Articulatory synthesis attempts to simulate the physical processes involved in speech production, modeling the movements of the articulatory organs (such as the tongue, lips, and vocal cords). It simulates the transfer function of the vocal tract to generate speech sounds based on articulatory gestures and acoustic properties. This approach aims to capture the underlying physiology of speech production, allowing for detailed control over articulatory features and acoustic output.

Formant Synthesis

Formant synthesis focuses on synthesizing speech by generating and manipulating specific spectral peaks, known as formants, which correspond to resonant frequencies in the vocal tract. By controlling the frequencies and amplitudes of these formants, formant synthesis algorithms can produce speech sounds with different vowel qualities and articulatory characteristics. This approach is particularly well-suited for synthesizing vowels and steady-state sounds, but it may struggle with accurately reproducing transient sounds and complex articulatory features.

Cutting-Edge Text-to-Speech Solution

Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices which is the cheapest and most high-quality solution in the market. We cut your text-to-speech costs by up to 90%. Get human-like AI voices with our super fast / low latency API, with the option for per-word timestamps. With our simple easy-to-use API, you can give your LLM a voice with ease and offer this functionality at scale. If you are looking for cheap, scalable, realistic TTS to incorporate into your products, try our text-to-speech API for free today. Convert text into natural-sounding speech at an affordable and scalable price.

Speech synthesis technology has been a game-changer when it comes to making content more accessible for individuals with visual impairments. By using text-to-speech software, visually impaired individuals can now easily consume written content by listening to it. This eliminates the need for reading and allows them to have text read aloud to them directly from their devices. This innovation has opened up a world of opportunities for people with disabilities, enabling them to access information and tap into resources that were previously out of reach.

eLearning - Enhancing Educational Experiences with Voice Synthesizers

Voice synthesizers are revolutionizing the learning experience with the rise of eLearning platforms. Educators can now create interactive and engaging digital learning modules by leveraging speech synthesis technology.

By incorporating AI voices to read course content, voiceovers for videos, and audio elements, educators can create dynamic learning materials that enhance student engagement and bolster retention rates. This application of speech synthesis has proven to be instrumental in optimizing the learning process and fostering a more immersive educational environment.

Marketing and Advertising - Elevating Brand Communication Through Speech Synthesis

In the world of marketing, text-to-speech technology offers brands a powerful tool to enhance their communication strategies. By using synthetic voices that align with their brand identity, businesses can create voiceovers that resonate with their target audience.

Speech synthesis enables businesses to save costs that would otherwise be spent on hiring voice artists and audio engineers for advertising and promotional content. By integrating human-like voices into marketing videos and product demos, companies can effectively convey their brand message while saving on production expenses.

Content Creation - Crafting Engaging Multimedia Content with Speech Generation Tools

Another exciting application of speech generation technology is in the field of content creation. Content creators can now produce a wide range of multimedia content, including YouTube videos, audiobooks, podcasts, and more, using speech synthesis tools.

These tools enable creators to generate high-quality audio content that is engaging and captivating for their audience. By leveraging speech synthesis, content creators can explore new avenues of creativity and enhance the overall quality of their multimedia projects.

1. Unreal Speech: Cheap, Scalable, and Realistic TTS Synthesizer

Unreal Speech offers a low-cost, highly scalable text-to-speech API with natural-sounding AI voices, making it the cheapest and most high-quality solution in the market. It cuts your text-to-speech costs by up to 90%. With their super-fast API, you can get human-like AI voices with the option for per-word timestamps. The easy-to-use API allows you to give your LLM a voice effortlessly, offering this functionality at scale. If you are looking for cheap, scalable, and realistic TTS to incorporate into your products, Unreal Speech is the way to go.

2. Amazon Polly: Cloud-Based TTS Synthesizer

Amazon Polly's cloud-based TTS API uses Speech Synthesis Markup Language (SSML) to generate realistic speech from text. This enables users to integrate speech synthesis into applications seamlessly, enhancing accessibility and engagement.

3. Microsoft Azure: RESTful Architecture for TTS

Microsoft Azure's text-to-speech API follows a RESTful architecture for its text-to-speech interface. This cloud-based service supports flexible deployment, allowing users to run TTS at data sources.

4. Murf: Customizable High-Quality TTS Synthesizer

Murf is popular for its high-quality voiceovers and its ability to customize speech to a remarkable extent. It offers a unique voice model that delivers a lifelike user experience.

5. Speechify: Powerful TTS App Using AI

Speechify is a powerful text-to-speech app written in Python using artificial intelligence. It can help you convert any written text into natural-sounding speech.

6. IBM Watson Text to Speech: High-Quality, Natural-Sounding TTS

IBM Watson is known for its high-quality, natural-sounding voices. It provides a unique API that can be used in several programming languages, including Python.

7. Google Cloud Text to Speech: Global TTS Synthesizer

Google Cloud Text to Speech utilizes Google's powerful AI and machine learning capabilities to provide highly realistic voices. Supporting numerous languages and dialects, it is suitable for global enterprises.

Try Unreal Speech for Free Today — Affordably and Scalably Convert Text into Natural-Sounding Speech with Our Text-to-Speech API

Unreal Speech offers a cost-effective and scalable text-to-speech API with natural-sounding AI voices. It provides the cheapest and most high-quality solution in the market, reducing text-to-speech costs by up to 90%. With its super-fast/low latency API, Unreal Speech delivers human-like AI voices with the option for per-word timestamps. Its simple and easy-to-use API allows for giving your LLM a voice and offering this functionality at scale. If you are looking for an affordable, scalable, and realistic TTS solution to incorporate into your products, try Unreal Speech's text-to-speech API for free today to convert text into natural-sounding speech.

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• Published: 24 April 2019

Speech synthesis from neural decoding of spoken sentences

• Gopala K. Anumanchipalli 1 , 2   na1 ,
• Josh Chartier 1 , 2 , 3   na1 &
• Edward F. Chang 1 , 2 , 3  

Nature volume  568 ,  pages 493–498 ( 2019 ) Cite this article

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• Sensorimotor processing

Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. Decoding speech from neural activity is challenging because speaking requires very precise and rapid multi-dimensional control of vocal tract articulators. Here we designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. Intermediate articulatory dynamics enhanced performance even with limited data. Decoded articulatory representations were highly conserved across speakers, enabling a component of the decoder to be transferrable across participants. Furthermore, the decoder could synthesize speech when a participant silently mimed sentences. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication.

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The speech neuroprosthesis

A neural speech decoding framework leveraging deep learning and speech synthesis

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity

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The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

We thank M. Leonard, N. Fox and D. Moses for comments on the manuscript and B. Speidel for his help reconstructing MRI images. This work was supported by grants from the NIH (DP2 OD008627 and U01 NS098971-01). E.F.C. is a New York Stem Cell Foundation-Robertson Investigator. This research was also supported by The William K. Bowes Foundation, the Howard Hughes Medical Institute, The New York Stem Cell Foundation and The Shurl and Kay Curci Foundation.

Reviewer information

Nature thanks David Poeppel and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

These authors contributed equally: Gopala K. Anumanchipalli, Josh Chartier

Authors and Affiliations

Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA

Gopala K. Anumanchipalli, Josh Chartier & Edward F. Chang

Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

University of California Berkeley and University of California San Francisco Joint Program in Bioengineering, Berkeley, CA, USA

Josh Chartier & Edward F. Chang

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Contributions

G.K.A., J.C. and E.F.C. conceived the study; G.K.A. inferred articulatory kinematics; G.K.A. and J.C. designed the decoder; J.C. performed decoder analyses; G.K.A., E.F.C. and J.C. collected data and prepared the manuscript; E.F.C. supervised the project.

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Correspondence to Edward F. Chang .

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Extended data figures and tables

Extended data fig. 1 median original and decoded spectrograms..

a , b , Median spectrograms, time-locked to the acoustic onset of phonemes from original ( a ) and decoded ( b ) audio (/i/, n  = 112; /z/, n  = 115; /p/, n  = 69, /ae/, n  = 86). These phonemes represent the diversity of spectral features. Original and decoded median phoneme spectrograms were well-correlated (Pearson’s r  > 0.9 for all phonemes, P  = 1 × 10 −18 ).

Extended Data Fig. 2 Transcription WER for individual trials.

a , b , WERs for individually transcribed trials for pools with a size of 25 ( a ) or 50 ( b ) words. Listeners transcribed synthesized sentences by selecting words from a defined pool of words. Word pools included correct words found in the synthesized sentence and random words from the test set. One trial is one transcription of one listener of one synthesized sentence.

Extended Data Fig. 3 Electrode array locations for participants.

MRI reconstructions of participants’ brains with overlay of electrocorticographic electrode (ECoG) array locations. P1–5, participants 1–5.

Extended Data Fig. 4 Decoding performance of kinematic and spectral features.

Data from participant 1. a , Correlations of all 33 decoded articulatory kinematic features with ground-truth ( n  = 101 sentences). EMA features represent x and y coordinate traces of articulators (lips, jaw and three points of the tongue) along the midsagittal plane of the vocal tract. Manner features represent complementary kinematic features to EMA that further describe acoustically consequential movements. b , Correlations of all 32 decoded spectral features with ground-truth ( n  = 101 sentences). MFCC features are 25 mel-frequency cepstral coefficients that describe power in perceptually relevant frequency bands. Synthesis features describe glottal excitation weights necessary for speech synthesis. Box plots as described in Fig.  2 .

Extended Data Fig. 5 Comparison of cumulative variance explained in kinematic and acoustic state–spaces.

For each representation of speech—kinematics and acoustics—a principal components analysis was computed and the explained variance for each additional principal component was cumulatively summed. Kinematic and acoustic representations had 33 and 32 features, respectively.

Extended Data Fig. 6 Decoded phoneme acoustic similarity matrix.

Acoustic similarity matrix compares acoustic properties of decoded phonemes and originally spoken phonemes. Similarity is computed by first estimating a Gaussian kernel density for each phoneme (both decoded and original) and then computing the Kullback–Leibler (KL) divergence between a pair of decoded and original phoneme distributions. Each row compares the acoustic properties of a decoded phoneme with originally spoken phonemes (columns). Hierarchical clustering was performed on the resulting similarity matrix. Data from participant 1.

Extended Data Fig. 7 Ground-truth acoustic similarity matrix.

The acoustic properties of ground-truth spoken phonemes are compared with one another. Similarity is computed by first estimating a Gaussian kernel density for each phoneme and then computing the Kullback–Leibler divergence between a pair of a phoneme distributions. Each row compares the acoustic properties of two ground-truth spoken phonemes. Hierarchical clustering was performed on the resulting similarity matrix. Data from participant 1.

Extended Data Fig. 8 Comparison between decoding novel and repeated sentences.

a , b , Comparison metrics included spectral distortion ( a ) and the correlation between decoded and original spectral features ( b ). Decoder performance for these two types of sentences was compared and no significant difference was found ( P  = 0.36 ( a ) and P  = 0.75 ( b ), n  = 51 sentences, Wilcoxon signed-rank test). A novel sentence consists of words and/or a word sequence not present in the training data. A repeated sentence is a sentence that has at least one matching word sequence in the training data, although with a unique production. Comparison was performed on participant 1 and the evaluated sentences were the same across both cases with two decoders trained on differing datasets to either exclude or include unique repeats of sentences in the test set. ns, not significant; P  > 0.05. Box plots as described in Fig.  2 .

Extended Data Fig. 9 Kinematic state–space trajectories for phoneme-specific vowel–consonant transitions.

Average trajectories of principal components 1 (PC1) and 2 (PC2) for transitions from either a consonant or a vowel to specific phonemes. Trajectories are 500 ms and centred at transition between phonemes. a , Consonant to corner vowels ( n  = 1,387, 1,964, 2,259, 894, respectively, for aa, ae, iy and uw). PC1 shows separation of all corner vowels and PC2 delineates between front vowels (iy, ae) and back vowels (uw, aa). b , Vowel to unvoiced plosives ( n  = 2,071, 4,107 and 1,441, respectively, for k, p and t). PC1 was more selective for velar constriction (k) and PC2 for bilabial constriction (p). c , Vowel to alveolars ( n  = 3,919, 3,010 and 4,107, respectively, for n, s and t). PC1 shows separation by manner of articulation (nasal, plosive or fricative) whereas PC2 is less discriminative. d , PC1 and PC2 show little, if any, delineation between voiced and unvoiced alveolar fricatives ( n  = 3,010 and 1,855, respectively, for s and z).

Supplementary information

Supplementary information.

This file contains: a) Place-manner tuples used to augment EMA trajectories; b) Sentences used in listening tests Original Source: MOCHA-TIMIT20 dataset; c) Class sizes for the listening tests; d) Transcription interface for the intelligibility assessment; and e) Number of listeners used for intelligibility assessments.

Reporting Summary

Supplemental video 1: examples of decoded kinematics and synthesized speech production.

The video presents examples of synthesized audio from neural recordings of spoken sentences. In each example, electrode activity corresponding to a sentence is displayed (top). Next, simultaneous decoding of kinematics and acoustics are visually and audible presented. Decoded articulatory movements are displayed (middle left) as the synthesized speech spectrogram unfolds. Following the decoding, the original audio, as spoken by the patient during neural recording, is played. Lastly, the decoded movements and synthesized speech is once again presented. This format is repeated for a total of five examples (from participants P1 and P2). On the last example, kinematics and audio are also decoded and synthesized for silently mimed speech.

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Anumanchipalli, G.K., Chartier, J. & Chang, E.F. Speech synthesis from neural decoding of spoken sentences. Nature 568 , 493–498 (2019). https://doi.org/10.1038/s41586-019-1119-1

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Issue Date : 25 April 2019

DOI : https://doi.org/10.1038/s41586-019-1119-1

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The Ultimate Guide to Speech Synthesis in 2024

We've reached a stage where technology can mimic human speech with such precision that it's almost indistinguishable from the real thing. Speech synthesis, the process of artificially generating speech, has advanced by leaps and bounds in recent years, blurring the lines between what's real and what's artificially created. In this blog, we'll delve into the fascinating world of speech synthesis, exploring its history, how it works, and what the future holds for this cutting-edge technology. You can see speech sythesis in action with Murf studio for free.

Try Murf for free

Table of Contents

What is speech synthesis, text to written words, words to phonemes, concatenative, articulatory, assistive technology, marketing and advertising, content creation, software that use speech synthesis, why is murf the best speech synthesis software, what is speech synthesis, why is speech synthesis important, where can i use speech synthesis, what is the best speech synthesis software.

Speech synthesis, in essence, is the artificial simulation of human speech by a computer or any advanced software. It's more commonly also called text to speech . It is a three-step process that involves:

Contextual assimilation of the typed text

Mapping the text to its corresponding unit of sound

Generating the mapped sound in the textual sequence by using synthetic voices or recorded human voices

The quality of the human speech generated depends on how well the software understands the textual context and converts it into a voice.

Today, there is a multitude of options when it comes to text to speech software. They all provide different (and sometimes unique) features that help enhance the quality of synthesized speech. 

Speech generation finds extensive applications in assistive technologies, eLearning, marketing, navigation, hands-free tech, and more. It helps businesses with the cost-optimization of their marketing campaigns and assists those with vision impairments to 'read' text by hearing it read aloud, among other things. Let's understand how this technology works in more detail.

How Does Speech Synthesis Work?

The process of voice synthesis is quite interesting. Speech synthesis is done in three simple steps:

Text-to-word conversion

Word-to-phoneme conversion

Phoneme-to-sound conversion

Text to audio conversion happens within seconds, depending on the accuracy and efficiency of the software in use. Let's understand this process.

Before input text can be completely converted into intelligible human speech,   voice synthesizers must first polish and 'clean up' the entered text. This process is called 'pre-processing' or 'normalization.'

Normalization helps the TTS systems understand the context in which a text needs to be converted into synthesized speech. Without normalization, the converted speech likely ends up sounding unnatural or like complete gibberish.

To understand better, consider the case of abbreviations: "St." is read as "Saint." Without normalization, the software would just read it according to the phonetic rules instead of contextual insight. This may lead to errors.

The second step in text to speech conversion is working with the normalized text and locating the phonemes for each one. Every TTS software has a library of phonemes that corresponds to specific written words. A phoneme is a unique unit of sound that is attributed to a particular word in a language. It helps the text to speech software distinguish one word from another in any language.

When the software receives normalized input, it immediately begins locating the respective phonemes and pieces together bits of sound. However, there's one more catch involved: not all the words that are written the same are read the same way. So, the software looks up the context of the entire sentence to determine the most suitable prosody for a word and selects the right phonemes for output.

For example, "lead" can be read in two ways—"ledd" and "leed." The software selects the most suitable phoneme depending on the context in which the sentence is written.

Phonemes to Sounds

The final step is converting phonemes to sounds. While phonemes determine which sound goes with which word, the software is yet to  produce  any sound at all. There are three ways that the software produces audio waveforms:

This is the method where the software uses pre-recorded bits of the human voice for output. The software works by understanding the recorded snippets and rearranging them according to the list of phonemes it created as the output speech.

The formant method is similar to the way any other electronic device generates sound. By mimicking the frequency, wavelengths, pitches, and other properties of the phonemes in the generated list, the software can generate its own sound. This method is more effective than the concatenative one.

This is the most complex kind of custom speech synthesizer chip that exists (aside from the natural human voicebox) and is capable of mimicking human voice in surprising closeness.

Applications of Speech Synthesis

Speech generation isn't just made for individuals or businesses: it's a noble and inclusive technology that has generated a positive wave across the world by allowing the masses to 'read' by 'listening.' Some of the most notable speech synthesis applications are:

One of the most beneficial speech generation applications   is in assistive technology. According to data from WHO , there are about 2.2 billion people with some form of vision impairment worldwide. That's a lot of people, considering how important reading is for personal development and betterment.

With text to speech software, it has now become possible for these masses to consume typed content by listening to it. Text to speech eliminates the need for reading for visually-impaired people altogether. They can simply listen to the text on the screen or scan a piece of text onto their mobile devices and have it read aloud to them.

eLearning has been on a constant rise since the pandemic restricted most of the world's population to their homes. Today, people have realized how convenient it is to learn new concepts through eLearning videos and explainer videos .

Educators use voice synthesizers to create digital learning modules for learners, enabling a more immersive and engaging learning experience and environment for them. This catalysis has proved to be elemental in improving cognition and retention amongst students.

eLearning courses use speech synthesizers in the following ways:

Deploy AI voices to read the course content out loud

Create voiceovers for video and audio

Create learning prompts

Marketing and advertising are niches that require careful branding and representation. Text to speech gives brands the flexibility to create voiceovers in voices that represent their brand perfectly.

Additionally, speech synthesis helps businesses save a lot of money as well. By adding synthetic, human-like voices to their advertising videos and product demos , businesses save the expenses required for hiring and paying:

Audio engineers

Voice artists

AI voices also help save time while editing the script, eliminating the need to re-record an artist's voice with a new script. The text to speech tool can work with the text to produce audio through the edited script.

One of the most interesting applications of speech generation tools is the creation of video and audio content that is highly engaging. For example, you can create YouTube videos ,  audiobooks ,  podcasts,  and even lyrical tracks using these tools.

Without investing in voice artists, you can leverage hundreds of AI voices and edit them to your preferences. Many TTS tools allow you to adjust:

The pitch of the AI voice

Reading speed

This enables content creators to tailor AI voices to the needs and nature of their content and make it more impactful and engaging.

Natural Readers

Well Said Labs

Amazon Polly

When it comes to TTS, the two most important factors are the quality of output and its brand fit. These are the aspects that Murf helps your business get right with its text to speech modules that have customization capabilities second to none.

Some of the key features and capabilities of the Murf platform are:

Voice editing with adjustments to pitch, volume, emphasis, intonation, pause, speed, and emotion

Voice cloning feature for enterprises that allows them to create a custom voice that is an exact clone of their brand voice for any commercial requirement. 

Voice changer that lets you convert your own recorded voice to a professional sounding studio quality voiceover

Wrapping Up

If you've found yourself needing a voiceover for whichever purpose, text to speech (or speech generation) is your ideal solution. Thankfully, Murf covers all the bases while delivering exemplary performance, customizability, high quality, and variety in text to speech, which makes this platform one of the best in the industry. To generate speech samples for free, visit Murf today.

Speech synthesis is the technology that generates spoken language as output by working with written text as input. In other words, generating text from speech is called speech synthesis. Today, many software offer this functionality with varying levels of accuracy and editability.

Speech generation has become an integral part of countless activities today because of the convenience and advantages it provides. It's important because:

It helps businesses save time and money.

It helps people with reading difficulties understand text.

It helps make content more accessible.

Speech synthesis can be used across a variety of applications:

To create audiobooks and other learning media

In read-aloud applications to help people with reading, vision, and learning difficulties

In hands-free technologies like GPS navigation or mobile phones

On websites for translations or to deliver the key information audibly for better effect

…and many more.

Murf AI is the best TTS software because it allows you to hyper-customize your AI voices and mold them according to your voiceover needs. It also provides you with a suite of tools to further purpose your AI voices for applications like podcasts, audiobooks, videos, audio, and more.

You should also read:

How to create engaging videos using TikTok text to speech

An in-depth Guide on How to Use Text to Speech on Discord

Medical Text to Speech: Changing Healthcare for the Better

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Join the community, add a new evaluation result row, text-to-speech synthesis.

93 papers with code • 6 benchmarks • 17 datasets

Text-To-Speech Synthesis is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.

Benchmarks Add a Result

Most implemented papers

Fastspeech 2: fast and high-quality end-to-end text to speech.

In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e. g., pitch, energy and more accurate duration) as conditional inputs.

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.

Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units.

FastSpeech: Fast, Robust and Controllable Text to Speech

In this work, we propose a novel feed-forward network based on Transformer to generate mel-spectrogram in parallel for TTS.

Efficient Neural Audio Synthesis

The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.

Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram

We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

Clone a voice in 5 seconds to generate arbitrary speech in real-time

FastSpeech: Fast,Robustand Controllable Text-to-Speech

Compared with traditional concatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust (i. e., some words are skipped or repeated) and lack of controllability (voice speed or prosody control).

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e. g., mel-spectrogram) given a music score.

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SpeechSynthesis

Baseline widely available.

This feature is well established and works across many devices and browser versions. It’s been available across browsers since September 2018 .

• See full compatibility
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The SpeechSynthesis interface of the Web Speech API is the controller interface for the speech service; this can be used to retrieve information about the synthesis voices available on the device, start and pause speech, and other commands besides.

Instance properties

SpeechSynthesis also inherits properties from its parent interface, EventTarget .

A boolean value that returns true if the SpeechSynthesis object is in a paused state.

A boolean value that returns true if the utterance queue contains as-yet-unspoken utterances.

A boolean value that returns true if an utterance is currently in the process of being spoken — even if SpeechSynthesis is in a paused state.

Instance methods

SpeechSynthesis also inherits methods from its parent interface, EventTarget .

Removes all utterances from the utterance queue.

Returns a list of SpeechSynthesisVoice objects representing all the available voices on the current device.

Puts the SpeechSynthesis object into a paused state.

Puts the SpeechSynthesis object into a non-paused state: resumes it if it was already paused.

Adds an utterance to the utterance queue; it will be spoken when any other utterances queued before it have been spoken.

Listen to this event using addEventListener() or by assigning an event listener to the oneventname property of this interface.

Fired when the list of SpeechSynthesisVoice objects that would be returned by the SpeechSynthesis.getVoices() method has changed. Also available via the onvoiceschanged property.

First, a simple example:

Now we'll look at a more fully-fledged example. In our Speech synthesizer demo , we first grab a reference to the SpeechSynthesis controller using window.speechSynthesis . After defining some necessary variables, we retrieve a list of the voices available using SpeechSynthesis.getVoices() and populate a select menu with them so the user can choose what voice they want.

Inside the inputForm.onsubmit handler, we stop the form submitting with preventDefault() , create a new SpeechSynthesisUtterance instance containing the text from the text <input> , set the utterance's voice to the voice selected in the <select> element, and start the utterance speaking via the SpeechSynthesis.speak() method.

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AI Voice Generator

Cut costs, not quality - craft studio grade voiceovers with our ai voice generator in minutes.

Our AI Voice Generator is powered by sophisticated Artificial Intelligence algorithms trained on professional voice actors. This is why we are able to offer AI-generated voices so realistic you’ll have to pinch yourself.

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Trusted by hundreds of leading brands

Some ai voices sound good — the synthesys difference is that ours sound human.

Forget about expensive equipment and logistics hassles. Our AI avatars will present in your videos at a fraction of the cost.

Less time spent hiring artists means more time for building your brand

Forget paying for studio time and vetting voice actors. Synthesys free AI voice generator gives you the world-class quality of a professional recording studio in minutes.

Wide Range of Accents and Languages

We offer more than 370 voices in 140+ different languages, both male and female . This way, you can be sure that you will find a voice that will fit your brand and communicate globally.

Advanced Multilingual Voice Cloning

Replicate voices in multiple languages with our cutting-edge voice cloning feature . Perfect for creating consistent branding across different markets and languages.

Easy Text-to-Speech API Integration

Integrate lifelike speech capabilities into your applications effortlessly with our robust Text-to-Speech API – enabling seamless, scalable voice solutions across platforms.

Powerful. Flexible. Ridiculously easy to use

Turning any text into the kind of elite natural-sounding speech your brand deserves is as simple as clicking a button with Synthesys AI voice generator.

But don’t just take our word for it. Why not try it out yourself?

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No matter what you need an ai voice for, synthesys ai voice generator can handle it.

Don’t settle for anything less than complete customisability

At Synthesys, we like to go above and beyond. That’s why we built our AI text-to-speech tool to be as flexible as your brand deserves.

Emphasize specific sentences to evoke a wide range of real emotions, like passionate, joyful, confident, angry, and more

Use Preview mode to get an instant insight into how your voiceover will sound

Control the narrative with Speed & Pitch and add life to the end result with stresses on particular syllables

Add in pauses where appropriate to give your voiceover a truly human feel

The future of AI voices is here, and it looks pretty good

Casting aside cookie-cutter AI voice generators with robotic intonations, Synthesys brings you voices that are remarkably natural, persuasive, and tailored to foster genuine connections with your audience.

Still in doubt? Explore the examples below to experience it firsthand

The modern world is more connected than ever, and being understood has never been more important

That's why Synthesys AI Voice Generator offers hyper-realistic synthetic AI-generated voices in more than 140 languages.

Australian English

British english, don’t take our word for it.

Check out what our users have to say about working with Synthesys AI Studio

I never thought it was possible to create such high-quality videos without any prior experience in animation. Thanks to Synthesys, I was able to make amazing videos with ai-avatars and voiceovers in just a few minutes! It's the only AI content suite I'll ever need.

Paul Mitchel

As a content creator, I'm always looking for ways to improve my workflow and the quality of my content. Synthesys has been a game-changer for me. With just a few clicks, I can create amazing videos with voiceovers and ai-avatars. It's made my life so much easier and my content so much better.

I was skeptical at first, but after using Synthesys for a few weeks, I'm a true believer. The AI technology is incredible - it can turn images and voiceovers into amazing videos that look like they were created by a professional.

Cameron Williamson

Commercial Director

What you can create with Synthesys's software is nothing short of incredible! This is State Of The Art. There's nothing else that even comes close, as far as I know, and certainly not for the relatively small investment. Even better, the program's creators continue updating and upgrading the product, as the technology expands, at no extra cost! Try it, and be amazed at the possibilities!

Phillip Wilkinson

My experience with Synthesys AI Studio is very positive! They create Astounding products that blows my mind, in fact you might say they do the impossible, They are the very, very good at what they do! I think I have nearly all of their products to date and intend to purchase more!

From the start Synthesys has been delivering a quality product. The quality of the "actors" and the voices produced has been top-notch. And the updates and upgrades have been phenomenal. I am more than happy to continue using this platform.

Need Help with Our AI Voice Generator?

If you can't find your answer here, email [email protected] for additional support.

What is an AI Voice Generator?

An AI voice generator is a state-of-the-art technology that uses artificial intelligence (AI) to create voice recordings or speech that sounds human. These systems synthesize natural-sounding speech by analyzing large datasets of human voices through deep learning algorithms. AI voice generators can be used for various tasks, such as creating text-to-speech conversion solutions and voiceovers for movies and screen captures. They make producing high-quality audio content straightforward since they can imitate various accents, languages, and speech patterns. With its realistic and adaptable AI-generated voices, this technology revolutionizes sectors like accessibility services, media production, and content creation.

What is an AI Voice?

AI voice refers to a synthetic or computer-generated voice created using sophisticated algorithms and machine learning models. The AI voices' emulation of human voices makes speaking convincingly and naturally possible. Text-to-speech software, voice assistants, virtual CSRs, and content production are just a few of the industries they find use in. AI voices are flexible tools for information delivery, improving user experiences, and automating spoken communication chores since they can be tailored for various accents, languages, and tones.

How Do AI Voice Generators Work?

AI voice synthesizers use neural networks and deep learning techniques to mimic human speech. At first, these AI voice generators are trained on large datasets of human voice recordings to acquire phonemes, intonations, and speech patterns. After training, these models can anticipate the best phonetic and prosodic components to turn text input into synthetic voice. Pitch, tone, and tempo can all be changed to produce a variety of voices. Certain models (e.g., Synthesys) produce natural speech by combining phoneme sequences with text. With its natural-sounding synthetic voice, the output can be utilized for many purposes, such as voiceovers and text-to-speech. Here's a detailed rundown of how they function: Text processing — Written text is fed into the system at the start. This content may be presented in paragraphs, phrases, or even longer papers. Text analysis — The AI voice generator analyzes the text to determine its linguistic structure, including word order, punctuation, and grammar conventions. Sentence boundaries, parts of speech, and other linguistic components are also be identified at this step. Phonetic conversion — The AI then determines the text's phonetic representation. This entails dissecting words into their constituent phonemes, a language's smallest sound units. Voice selection — Selecting from various voices, dialects, and accents is the next option for the user, depending on the particular AI voice generator. The AI model that generates the voice can significantly impact the output's naturalness and quality. Natural Language Processing — The AI uses natural language processing techniques to comprehend semantics and context. This aids in choosing the proper tempo, stress, and intonation—all of which are essential for the generated speech to sound realistic. Voice synthesis — Combining phonetic components, prosody (intonation, rhythm, and pitch), and language context allows the AI to produce speech. The audio waveform is generated by deep learning models such as Transformer-based architectures, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Audio rendering — The audio waveform is then created from the synthesized speech. The digital audio data that can be played on speakers or headphones is represented by this waveform. Output — Delivering the created audio to the user is the last stage. This could take the shape of an audio file that can be downloaded, audio that can be streamed, or an application or service integration. Customization — customization is a key feature of modern AI voice generators. Users now have the ability to tweak elements like speech speed, pauses, pitch, and tone to better suit their preferences. These customization options have opened up new possibilities for users to personalize their AI-generated voices. Integration — integration is another exciting aspect of AI voice generators. These systems can seamlessly integrate into a range of applications, from virtual assistants and accessibility tools to e-learning platforms and content creation software. This integration capability makes AI-generated voices a valuable addition to various fields, enhancing the user experience in each of these areas. Over the past few years, AI voice generators have made significant advancements, resulting in remarkably natural-sounding speech. They have found their footing in diverse sectors, including education, entertainment, accessibility, and customer service. This progress has made synthetic speech that closely resembles human speech more accessible and adaptable than ever before.

How Long Does It Take To Synthesize Text to Speech?

Text complexity, speech synthesis engine performance, and text length are some variables that affect how long it takes to synthesize text into speech. Modern AI-based text-to-speech systems can produce speech for short to medium-length texts almost instantly, usually in a few seconds. However, the synthesis process may take a little longer—typically a few seconds to a minute—for longer and more complicated texts. Advances in AI technology have significantly shortened the time required for text-to-speech conversion, making it a quick and efficient process for various applications, including voice assistants and content production.

How is Voice Generation Time Calculated?

The text's intricacy, the AI voice model's quality, and the hardware's processing capacity affect how long it takes to generate an audio file. Since it's usually monitored in real-time, processing a minute's worth of voice creation takes roughly a minute. Dedicated gear and speedier CPUs, though, can expedite the procedure. Furthermore, cloud-based AI services could provide different processing speeds depending on server traffic. Longer texts and more complex voice models will also lengthen the generation time. In conclusion, real-time processing is the baseline, while text complexity, software, and hardware affect generation time.

Why Should I Use An AI Voice Generator Instead Of Hiring Voice Artists?

AI voice generators provide economical and practical options for content creation and voiceovers. They save time and money by offering instant access to various voices, languages, and accents. AI speech generators can produce content in minutes instead of paying professional voice actors; therefore, projects can be completed quickly. They also provide possibilities for pitch, tone, and pause adjustments, as well as speed, pronunciation, and emotions, resulting in adaptable and realistic-sounding results. Professional voice actors provide a personal touch, but AI voice generators are a realistic option for content creators seeking quality and ease, especially when working on tight deadlines or budgets.

Why Choose Synthesys AI Studio?

Synthesys AI Studio is a great choice for businesses and creators who want high-quality AI voices for their projects. It's fairly easy to use and comes with one of the biggest selections of voices to choose from (300+ voices). There's also a special feature to tweak how the voices sound, including their speed and pitch. Finally, Synthesys AI Studio supports over 140 languages, making it useful for many people around the world. So, if you want to add amazing AI voices to your work, whether it's for professional voiceovers, videos, or audio, Synthesys AI Studio is a good option.

Can I Try Synthesys Studio AI Voice Generator For Free?

Unlike other platforms, you can use Synthesys Studio AI Voice Generator's free trial without registering for an account or adding your credit card information. Although free, there are certain restrictions, like a monthly cap on the amount of audio rendered in minutes and an artificial intelligence script assistant with incredibly realistic voices. If the free trial does not meet your needs completely, you can always select from other plans with more perks (Premium and Professional) to enhance your material further.

What Languages Does Synthesys AI Voice Generator Support?

Synthesys AI Voice Generator ensures accessibility for all and sundry with support for 140 languages, including English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese (Simplified and Traditional), Japanese, Korean, Arabic, and many more. You can find all languages here . This broad language support makes it possible for users to produce voiceovers, speech synthesis, and material in various languages and accents, appealing to a wide range of users and making it a flexible tool for several uses.

Can I Use The Voices For Commercial Purposes?

The license agreements and terms of service for the particular AI voice generator software you are using will dictate whether or not you can use AI-generated voices for commercial purposes. The professional and premium plans from Synthesys include commercial licenses that let you utilize the voices for profit-making projects like marketing films, commercials, and other types of content. Nevertheless, there are restrictions on commercial use with our free edition and basic plan. It's vital to ensure you adhere to any usage restrictions by carefully reading the terms and licensing agreements of the plan you intend to use. You should subscribe to a premium or professional plan to take full advantage of our AI voice generator platform and obtain full commercial rights to use AI-generated voices in your commercial projects.

Is Synthesys The Best AI Voice Generator?

Synthesys is a well-known text-to-voice generator founded in 2020 and known for producing natural, human-sounding, high-quality voice synthesis. Since then, Synthesys has made huge leaps in producing ultra life-like sound voices and improving voice quality to the point where it's difficult to distinguish between a real human voice and an AI-generated voice. While Synthesys AI voice generator has received praise for its functionality and usability, it's essential to keep in mind that "the best" AI voice generator could differ based on personal preferences and demands. Synthesys is adaptable for a range of applications since it provides a variety of speech styles, languages, and accents. With a user-friendly interface and multiple customization settings, you can customize the AI voiceovers through Synthesys as needed. However, the "best" option will vary depending on desired features, voice needs, and affordability. It is best to investigate and contrast several AI voice generators to see which best suits your specific project's requirements for creating content.

How Do I Generate An AI Voice?

Registering on Synthesys' website is the first step towards creating a realistic AI voice. Once you're in, type or paste the text you want to convert to speech. Next, select your preferred AI-generated voice from various voices with varying accents, languages, and genders. Adjust the speech tempo, pitch, emotions, and tone to ensure the voice sounds perfect. For more information, check out our best tips guide inside the app and the training sections. nce the text has been entered and the actor of your choice has been picked, just press the play button at the bottom and wait for a little while for the platform's AI voice technology to produce an audio file with the voice of your choice. After it's finished, you can download the audio files in MP3 format. In addition, AI voice actors can also be used in languages other than those in which speakers are trained, so accented speech will carry across speakers. If you want French-accented English, for example, you can use French actors. You may utilize this AI-generated voice in any project that calls for realistic and natural-sounding speech, such as voiceovers, screen recordings, business presentations, onboarding videos, training videos, or films. In the event that you desire more than you presently have, just remember to review our terms and pricing plans.

Does Synthesys Work Offline?

Cloud-based services are Synthesys' primary mode of operation. Processing and producing high-quality synthetic sounds and speech from text inputs requires robust servers and internet access. Synthesys relies on an internet connection because users usually access it via a web interface or API.

Can I Use Synthesys For YouTube Videos?

Certainly! You can absolutely use Synthesys for your YouTube videos. Our AI tool offers text-to-speech capabilities, allowing you to transform written content into natural-sounding speech. It's a real game-changer for YouTube content creators looking to add narration, voiceovers, or subtitles to their videos without the need for a human voice actor. With Synthesys, you can effortlessly create engaging and informative YouTube content by generating top-notch synthetic voices in multiple languages and accents. It's a fast and cost-effective way to enhance your video material and reach a global audience. Just input your script, pick a voice style that suits your video, and let Synthesys work its magic, delivering authentic, professional-sounding AI speech.

Do You Have A Text-To-Speech API?

Yes, Synthesys offers a text-to-speech API (Application Programming Interface) for seamlessly integrating its text-to-speech (TTS) capabilities into your projects.

Ready to start generating AI voiceovers so realistic you won’t be able to tell the difference?

ElevenLabs moves beyond speech with AI-generated Sound Effects

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After launching tools for text-to-speech and speech-to-speech synthesis, AI voice startup ElevenLabs is moving to the next target. The two-year-old startup founded by former Google and Palantir employees today announced the launch of a new text-to-sound AI offering called Sound Effects.

Available starting today on the ElevenLabs website, Sound Effects uses the startup’s in-house foundation model and allows creators to generate different types of audio samples by simply typing a description of their imagined sound.

The company first teased the tool in February with a post featuring Sora-generated clips, albeit enhanced with AI sound effects.

We were blown away by the Sora announcement but felt it needed something… What if you could describe a sound and generate it with AI? pic.twitter.com/HcUxQ7Wndg — ElevenLabs (@elevenlabsio) February 18, 2024

ElevenLabs partnered with Shutterstock to bring this product to life and expects to see adoption from creators across domains who are looking to enhance their content with immersive soundscapes.

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What to expect from ElevenLabs Sound Effects?

Currently, when creators want to add ambient noises to their content — such as social videos, games, movies and TV shows — the must either manually record them or buy/license audio files from different repositories on the internet.

The approach works, but you may not always find the audio you’re looking for from these sources, or have the budget to pay to record a new sound.

ElevenLabs’ new Sound Effects tool changes that, giving creators and production teams a way to get exactly what they want by simply typing it in plain, conversational English.

When a user enters a text prompt detailing the sound effect they are looking for, the model powering Sound Effects processes it and generates six unique audio samples to choose from.

The user can then listen to each of these and pick what works best for their project by downloading or storing it directly on ElevenLabs’ platform. 

VentureBeat got early access to the offering and found it was able to generate clear outputs in about 30-40 seconds. However, in our tests, Sound Effects generated just four options, not six.

This included a range of audio samples, covering standard ambient noises such as thunderstorms, doorbells and coins jingling to more complex ones like monkeys chattering, cars racing, people eating at a diner or a train coming to a halt.

Mati Staniszewski, CEO of ElevenLabs, told VentureBeat the tool can also go beyond a few-second-long sounds to produce longer audio samples such as instrumental music and character voices.

“It can generate instrumental music tracks up to 22 seconds with prompts like guitar loop, jazz saxophone solo, and music techno loop,” Staniszewski explained. “The model can also create a variety of character voices using prompts like ‘woman singing dancing in the sand, we watched the daylight end’ or ‘an ogre saying ‘stay away puny human’. You can even chain together sounds with prompts like ‘A joyful elderly woman says I’m so proud of you and then laughs.'”

While the company has not shared specifics of the model powering these capabilities, it did note that it is based on in-house research of the company and has been fine-tuned on Shutterstock’s audio library of licensed tracks. 

“The combined power of our rich and immersive library of tracks and this cutting-edge audio technology has enabled the creation of a true market first. We’re thrilled by the positive feedback from the early access community and look forward to seeing the wide array of projects they will create,” Aimee Egan, Chief Enterprise Officer at Shutterstock, said in a statement.

Goal to power creators worldwide

Since its inception two years ago, ElevenLabs has focused on developing and launching powerful AI audio capabilities.

The company first launched models for text-to-speech in different languages and then followed it up with a voice cloning product and AI Dubbing , a speech-to-speech conversion tool that allowed users to translate audio and video into 29 different languages whilst preserving the original speaker’s voice and emotions.

With the launch of Sound Effects today, it is extending this work, equipping creators with more tools to produce high-quality content.

Staniszewski hopes creators across domains will be able to use Sound Effects, including film and television studios, video game developers, marketers and social media content creators.

However, he did not share the names of the enterprises that have been alpha-testing the product thus far. 

Back in January, the company said it counts 41% of the Fortune 500 among its customers, including big names such as The Washington Post, Storytel and TheSoul Publishing.

As the next step, Staniszewski added, the company will also launch a music generation model as well as a voiceover studio offering, which is currently in alpha. The timeline for both remains unclear at this stage.

Other companies in the AI speech, sound and music generation space are Google, Meta, Suno, Pika , MURF.AI ,  Play.ht  and  WellSaid Labs . According to  Market US , the global market for such tools stood at $1.2 billion in 2022 and is estimated to touch nearly $5 billion in 2032, with a CAGR of slightly above 15.40%.

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Press Release Details

Nvidia brings ai assistants to life with geforce rtx ai pcs.

TAIPEI, Taiwan, June 02, 2024 (GLOBE NEWSWIRE) -- COMPUTEX -- NVIDIA today announced new NVIDIA RTX ™ technology to power AI assistants and digital humans running on new GeForce RTX ™ AI laptops.

NVIDIA unveiled Project G-Assist — an RTX-powered AI assistant technology demo that provides context-aware help for PC games and apps. The Project G-Assist tech demo debuted with ARK: Survival Ascended from Studio Wildcard. NVIDIA also introduced the first PC-based NVIDIA NIM™ inference microservices for the NVIDIA ACE digital human platform.

These technologies are enabled by the NVIDIA RTX AI Toolkit , a new suite of tools and software development kits that aid developers in optimizing and deploying large generative AI models on Windows PCs. They join NVIDIA’s full-stack RTX AI innovations accelerating over 500 PC applications and games and 200 laptop designs from manufacturers.

In addition, newly announced RTX AI PC laptops from ASUS and MSI feature up to GeForce RTX 4070 GPUs and power-efficient systems-on-a-chip with Windows 11 AI PC capabilities. These Windows 11 AI PCs will receive a free update to Copilot+ PC experiences when available.

“NVIDIA launched the era of AI PCs in 2018 with the release of RTX Tensor Core GPUs and NVIDIA DLSS,” said Jason Paul, vice president of consumer AI at NVIDIA. “Now, with Project G-Assist and NVIDIA ACE, we’re unlocking the next generation of AI-powered experiences for over 100 million RTX AI PC users.”

Project G-Assist, a GeForce AI Assistant AI assistants are set to transform gaming and in-app experiences — from offering gaming strategies and analyzing multiplayer replays to assisting with complex creative workflows. Project G-Assist is a glimpse into this future.

PC games offer vast universes to explore and intricate mechanics to master, which are challenging and time-consuming feats even for the most dedicated gamers. Project G-Assist aims to put game knowledge at players’ fingertips using generative AI.

Project G-Assist takes voice or text inputs from the player, along with contextual information from the game screen, and runs the data through AI vision models. These models enhance the contextual awareness and app-specific understanding of a large language model (LLM) linked to a game knowledge database, and then generate a tailored response delivered as text or speech.

NVIDIA partnered with Studio Wildcard to demo the technology with ARK: Survival Ascended . Project G-Assist can help answer questions about creatures, items, lore, objectives, difficult bosses and more. Because Project G-Assist is context-aware, it personalizes its responses to the player’s game session.

In addition, Project G-Assist can configure the player’s gaming system for optimal performance and efficiency. It can provide insights into performance metrics, optimize graphics settings depending on the user’s hardware, apply a safe overclock and even intelligently reduce power consumption while maintaining a performance target.

First ACE PC NIM Debuts NVIDIA ACE technology for powering digital humans is now coming to RTX AI PCs and workstations with NVIDIA NIM — inference microservices that enable developers to reduce deployment times from weeks to minutes. ACE NIM microservices deliver high-quality inference running locally on devices for natural language understanding, speech synthesis, facial animation and more.

At COMPUTEX, the gaming debut of NVIDIA ACE NIM on the PC will be featured in the Covert Protocol tech demo , developed in collaboration with Inworld AI. It now showcases NVIDIA Audio2Face ™ and NVIDIA Riva automatic speech recognition running locally on devices.

Windows Copilot Runtime to Add GPU Acceleration for Local PC SLMs Microsoft and NVIDIA are collaborating to help developers bring new generative AI capabilities to their Windows native and web apps. This collaboration will provide application developers with easy application programming interface (API) access to GPU-accelerated small language models (SLMs) that enable retrieval-augmented generation (RAG) capabilities that run on-device as part of Windows Copilot Runtime.

SLMs provide tremendous possibilities for Windows developers, including content summarization, content generation and task automation. RAG capabilities augment SLMs by giving the AI models access to domain-specific information not well represented in ‌base models. RAG APIs enable developers to harness application-specific data sources and tune SLM behavior and capabilities to application needs.

These AI capabilities will be accelerated by NVIDIA RTX GPUs, as well as AI accelerators from other hardware vendors, providing end users with fast, responsive AI experiences across the breadth of the Windows ecosystem.

The API will be released in developer preview later this year.

4x Faster, 3x Smaller Models With the RTX AI Toolkit The AI ecosystem has built hundreds of thousands of open-source models for app developers to leverage, but most models are pretrained for general purposes and built to run in a data center.

To help developers build application-specific AI models that run on PCs, NVIDIA is introducing RTX AI Toolkit — a suite of tools and SDKs for model customization, optimization and deployment on RTX AI PCs. RTX AI Toolkit will be available later this month for broader developer access.

Developers can customize a pretrained model with open-source QLoRa tools. Then, they can use the NVIDIA TensorRT ™ model optimizer to quantize models to consume up to 3x less RAM. NVIDIA TensorRT Cloud then optimizes the model for peak performance across the RTX GPU lineups. The result is up to 4x faster performance compared with the pretrained model.

The new  NVIDIA AI Inference Manager  SDK, now available in early access, simplifies the deployment of ACE to PCs. It preconfigures the PC with the necessary AI models, engines and dependencies while orchestrating AI inference seamlessly across PCs and the cloud.

Software partners such as Adobe, Blackmagic Design and Topaz are integrating components of the RTX AI Toolkit within their popular creative apps to accelerate AI performance on RTX PCs.

“Adobe and NVIDIA continue to collaborate to deliver breakthrough customer experiences across all creative workflows, from video to imaging, design, 3D and beyond,” said Deepa Subramaniam, vice president of product marketing, Creative Cloud at Adobe. “TensorRT 10.0 on RTX PCs delivers unprecedented performance and AI-powered capabilities for creators, designers and developers, unlocking new creative possibilities for content creation in industry-leading creative tools like Photoshop.”

Components of the RTX AI Toolkit, such as TensorRT-LLM, are integrated in popular developer frameworks and applications for generative AI, including Automatic1111, ComfyUI, Jan.AI, LangChain, LlamaIndex, Oobabooga and Sanctum.AI.

AI for Content Creation NVIDIA is also integrating RTX AI acceleration into apps for creators, modders and video enthusiasts.

Last year, NVIDIA introduced RTX acceleration using TensorRT for one of the most popular Stable Diffusion user interfaces, Automatic1111. Starting this week, RTX will also accelerate the highly popular ComfyUI, delivering up to a 60% improvement in performance over the currently shipping version, and 7x faster performance compared with the MacBook Pro M3 Max.

NVIDIA RTX Remix is a modding platform for remastering classic DirectX 8 and DirectX 9 games with full ray tracing, NVIDIA DLSS 3.5 and physically accurate materials. RTX Remix includes a runtime renderer and the RTX Remix Toolkit app, which facilitates the modding of game assets and materials.

Last year, NVIDIA made RTX Remix Runtime open source, allowing modders to expand game compatibility and advance rendering capabilities.

Since RTX Remix Toolkit launched earlier this year, 20,000 modders have used it to mod classic games , resulting in over 100 RTX remasters in development on the RTX Remix Showcase Discord .

This month, NVIDIA will make the RTX Remix Toolkit open source, allowing modders to streamline how assets are replaced and scenes are relit, increase supported file formats for RTX Remix’s asset ingestor and bolster RTX Remix’s AI Texture Tools with new models.

In addition, NVIDIA is making the capabilities of RTX Remix Toolkit accessible via a REST API, allowing modders to livelink RTX Remix to digital content creation tools such as Blender, modding tools such as Hammer and generative AI apps such as ComfyUI. NVIDIA is also providing an SDK for RTX Remix Runtime to allow modders to deploy RTX Remix’s renderer into other applications and games beyond DirectX 8 and 9 classics.

With more of the RTX Remix platform being made open source, modders across the globe can build even more stunning RTX remasters.

NVIDIA RTX Video , the popular AI-powered super-resolution feature supported in the Google Chrome, Microsoft Edge and Mozilla Firefox browsers, is now available as an SDK to all developers, helping them natively integrate AI for upscaling, sharpening, compression artifact reduction and high-dynamic range (HDR) conversion.

Coming soon to video editing software Blackmagic Design’s DaVinci Resolve and Wondershare Filmora, RTX Video will enable video editors to upscale lower-quality video files to 4K resolution, as well as convert standard dynamic range source files into HDR. In addition, the free media player VLC media will soon add RTX Video HDR to its existing super-resolution capability.

Learn more about RTX AI PCs and technology by joining NVIDIA at COMPUTEX .

About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in accelerated computing.

For further information, contact: Jordan Dodge NVIDIA Corporation +1-408-566-6792 [email protected]

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, performance, and availability of our products, services, and technologies, including NVIDIA RTX technology, GeForce RTX AI laptops, Project G-Assist, NVIDIA NIM inference microservices, NVIDIA ACE digital human platform, NVIDIA RTX AI Toolkit, GeForce RTX 4070 GPUs, RTX Tensor Core GPUs, DLSS, NVIDIA Audio2Face, NVIDIA Riva, NVIDIA TensorRT, NVIDIA AI Inference Manager, NVIDIA RTX Remix, NVIDIA DLSS 3.5, RTX Remix Runtime, and NVIDIA RTX Video; the benefits and impact of NVIDIA’s collaboration with third parties, and the features and availability of their services and offerings; third parties using or adopting NVIDIA’s products or technologies and the benefits thereof; RAG APIs enabling developers to harness application-specific data sources and tune SLM behavior and capabilities to application needs; and NVIDIA unlocking the next generation of AI-powered experiences for over 100 million RTX AI PC users, are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features, or functions set forth herein.

© 2024 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, Audio2Face, GeForce RTX, NVIDIA NIM, NVIDIA RTX and TensorRT are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at: https://www.globenewswire.com/NewsRoom/AttachmentNg/25d171ac-da6c-4ebb-880e-71d26b0f5f1e

NVIDIA RTX AI PC

NVIDIA's Project G-Assist is an RTX-powered AI assistant technology demo that provides context-aware help for PC games and apps.

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Computer Science > Computation and Language

Title: enhancing zero-shot text-to-speech synthesis with human feedback.

Abstract: In recent years, text-to-speech (TTS) technology has witnessed impressive advancements, particularly with large-scale training datasets, showcasing human-level speech quality and impressive zero-shot capabilities on unseen speakers. However, despite human subjective evaluations, such as the mean opinion score (MOS), remaining the gold standard for assessing the quality of synthetic speech, even state-of-the-art TTS approaches have kept human feedback isolated from training that resulted in mismatched training objectives and evaluation metrics. In this work, we investigate a novel topic of integrating subjective human evaluation into the TTS training loop. Inspired by the recent success of reinforcement learning from human feedback, we propose a comprehensive sampling-annotating-learning framework tailored to TTS optimization, namely uncertainty-aware optimization (UNO). Specifically, UNO eliminates the need for a reward model or preference data by directly maximizing the utility of speech generations while considering the uncertainty that lies in the inherent variability in subjective human speech perception and evaluations. Experimental results of both subjective and objective evaluations demonstrate that UNO considerably improves the zero-shot performance of TTS models in terms of MOS, word error rate, and speaker similarity. Additionally, we present a remarkable ability of UNO that it can adapt to the desired speaking style in emotional TTS seamlessly and flexibly.

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Celebrating customers’ journeys to AI innovation at Microsoft Build 2024

By Victoria Sykes Product Marketing Manager, Azure AI, Microsoft

Posted on May 30, 2024 5 min read

Ever since I started at Microsoft in August 2023, I was more than excited for Microsoft Build 2024, which wrapped up last week. Why? The achievements of our customers leveraging Azure AI to drive innovation across all industries are astounding, and I’ll happily take every opportunity to showcase and celebrate them. From enhancing productivity and creativity to revolutionizing customer interactions with custom copilots, our customers demonstrate the transformative power of generative AI and truly, brought Build 2024 to life. So, how’d they do it?  

Fostering creativity and innovation 

By creating Muse Chat, a copilot for coding and documentation, Unity Technologies showcases how AI can foster creativity in the gaming industry. This tool, developed with Azure OpenAI Service and Azure AI Content Safety, allows developers to create and perfect their own video games. At Build, Unity gave a live demo on Muse Chat during the Multimodel, Multimodal and Multiagent Innovation with Azure AI , which resulted in a video game prototype based on prompts, without typing a single line of code. Unity also provided a gaming station at our event celebration and gave out Unity trial subscriptions to developers.  

Pushing the boundaries of creative expression and efficiency, WPP explores the use of video, images, and speech to accelerate content creation with Azure OpenAI Service (GPT-4 with Vision). Generative AI and multimodality are central to their innovation. Additionally, WPP is a leader when it comes to accessibility. Featured in the Day 1 keynote and the Accessibility in the era of generative AI , WPP showcased their solutions with Seeing AI. 

Where innovators are creating the future

The New York City Department of Education is leveraging generative AI through Azure OpenAI Service to create a specialized teaching assistant capable of providing instant feedback and addressing student inquiries. This solution is developed using the Azure platform, ensuring seamless integration and enhanced educational support. During the Build session, safeguard your copilot with Azure AI , event-goers learned how NYC built this solution and how they are keeping the experience safe for teachers and students alike. 

Enhancing productivity and efficiency 

Utilizing AI Tax Assist with Azure OpenAI Service and Azure AI Studio, H&R Block is helping tax professionals and filers reduce the time and effort needed to file taxes. The integration of Azure AI Search for RAG exemplifies how AI can streamline complex processes to enhance productivity .  

Featured in the Azure AI Studio—creating and scaling your custom copilots session, Sweco focused on freeing up time for more creative solutions in client projects by developing SwecoGPT using Azure AI Studio. This digital assistant automates document creation and analysis, allowing consultants to quickly find critical project information and deliver more personalized services. As a result, consultants report increased productivity and added client value.  

By leveraging the full Microsoft Azure stack, Sapiens International enhances developer creativity and streamlines the development of automated customer solutions. The use of Azure Kubernetes Service and Azure-managed databases significantly improves tasks like underwriting, claims processing, and fraud detection. 

For PwC, the creation of ChatPwC, a scalable and secure GenAI solution using Azure OpenAI Service, Azure AI Search, and Azure AI Document Intelligence, has been a game-changer. This tool helps employees summarize, assess, and identify themes in client data, benefiting hundreds of thousands of employees daily. 

Transforming customer interactions 

Revolutionizing customer interactions, Vodafone introduced TOBi, a public-facing virtual assistant which efficiently handles calls and empowers agents through context-aware conversations and call transcriptions. SuperAgent, a company-wide internal conversational AI search copilot, enhances agent efficiency, helping customer journeys. 

To transform their MBUX Voice Assistant and dashcams, Mercedes-Benz integrates GPT-4 Turbo with Vision via Azure OpenAI Service. This technology enables the car to understand its surroundings and provide context for speech assistance with the ‘Hey Mercedes’ cue. In the new multimodal vision AI models and their practical applications breakout session, Mercedes showcased the power of multimodality by demonstrating the capabilities of ‘Hey Mercedes’ including the ability to add DALL-E generated images to vehicles’ dashboards by a simple voice command. 

Offering an immersive in-car infotainment system, TomTom utilizes Azure OpenAI Service, Azure Kubernetes Service, and CosmosDB. Their Digital Cockpit demonstrates the impact of advanced technologies in creating a seamless and enriched driving environment. In the TomTom brings AI-powered, talking cars to life with Azure session at Build, staff data scientist Massimiliano Ungheretti showcased how he led the development of the Digital Cockpit.  

Detecting suspicious activity by analyzing millions of daily transactions, Kinectify uses its AI-powered anti-money laundering (AML) risk management platform. Built with Azure Cosmos DB, Azure AI Services, and Azure Kubernetes Service, the platform is scalable and robust. 

Elevating business operations 

Featured in the Build sophisticated custom copilots with Azure OpenAI Assistants API breakout session, Coca-Cola aims to improve the productivity of its 30,000 associates by leveraging the Assistants API with GPT-4 Turbo for KO Assist, their genAI copilot. These standardized assistants help with enhanced business intelligence, data synthesis, strategic planning, and risk management across all departments. 

Increasing productivity and enabling more intuitive app development, Freshworks uses Freddy Copilot to provide conversational assistance and informed insights to employees and customers. This tool is supported by the Assistants API. Freshworks showcased Freddy Copilot in Build sophisticated custom copilots with Azure OpenAI Assistants API . 

Featured in the Day 2 keynote, the fastest growing consumer experience in history, OpenAI , now offers enhanced features like GPTs for customizing ChatGPT with external knowledge and Assistants API for developers to create AI-powered assistants with scaled-up external knowledge integration using retrieval augmented generation (RAG). Leveraging Azure AI Search, OpenAI API users can now upload 500 times more files, transforming it into a powerful retrieval system, solving significant present and future challenges. 

Thank you 

We are proud to celebrate the remarkable achievements of our customers at Microsoft Build 2024 and beyond . These examples underscore the transformative impact of Azure AI across various industries, showcasing how innovative solutions can revolutionize the way we act, think, and work.

As we continue to develop and support cutting-edge AI technologies, we are excited for even more groundbreaking advancements to come from our customers, driving the future of digital transformation and setting new standards for excellence. Together, we are shaping a smarter, more efficient, and more connected world.

Curious how your enterprise can unleash their potential with generative AI? With Azure, the opportunities are endless .

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