def of a speech community

• Acknowledgements
• 1. Language and Identity
• 1.1. What Is a Speech Community?
• 1.2. Coercive vs. Collaborative Relations
• 1.3. Language Minority Stories
• 2. Who Are English Learners?
• 2.1. Reflection Model
• 2.2. Inclusive Pedagogy
• 2.2. Makoto Critical Incident
• 2.3. Assumptions to Rethink about English Learners
• 2.4. Critical Learning Domains
• 3. Understanding Theory
• 3.1. Communication, Pattern, and Variability
• 3.2. Five Curriculum Guidelines
• 3.3. Indicators of Instructional Conversation (IC)
• 3.4. Indicators of the Standards for Effective Pedagogy
• 3.5. Standards for Effective Pedagogy
• 3.6. Examining Current Realities
• 4.1. Input and Native Language Acquisition
• 4.2. Input and Second Language Acquisition
• 4.3. The Interdependence Hypothesis
• 4.4. The Threshold Hypothesis
• 4.5. Vocabulary Development and Language Transfer
• 4.6. Text Modification
• 5. Interaction
• 5.1. Code Switching and Interaction
• 5.2. Characteristics of Modifications for Interaction
• 5.3. How Can Teachers Help Second Language Learners Begin to Communicate?
• 5.4. Classroom Routines and Participation Structures
• 5.5. We Can Talk: Cooperative Learning in the Elementary ESL Classroom
• 6. Stages of Development
• 6.1. Proficiency Levels Defined
• 7. Errors and Feedback
• 7.1. Points to Remember About Errors
• 7.2. Effective and Appropriate Feedback for English Learners
• 8. Types of Proficiencies
• 8.1. Fostering Second Language Development in Young Children
• 8.2. Instructional Conversation in Native American Classroom
• 8.3. Student Motivation to Learn
• 8.4. Language Learning Strategies: An Update
• 8.5. Three Misconceptions about Age and L2 Learning
• 9. Types of Performances
• 9.1. Understanding BICS and CALP
• 9.2. The Order of Acquisition and The Order of Use
• 9.3. Schumann's Acculturation Model
• 9.4. Implications From the Threshold and Interdependence Hypotheses
• 9.5. Lily Wong Fillmore’s Cognitive and Social Strategies for Second Language Learners
• 10. Classroom Practices and Language Acquisition
• Translations

What Is a Speech Community?

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What is vernacular?

A speech community is a group of people who share rules for conducting and interpreting at least one variety of a language or dialect. The term can be applied to a neighborhood, a city, a region or a nation. We all belong to at least one speech community. The earliest speech community we belong to is the one we share with our primary caregivers (usually our parents) and is the basis for some of the most intimate and long term relationships we form across our life. The rules and norms of this speech community show up in a dialect referred to as the vernacular, the most basic variety or dialect of language we command. Our vernacular speech is least susceptible to monitoring and least likely to change across our lifetime.

Most of us were immersed in language from our first awareness of the world around us. Since infants can hear the sound of their mother’s voice and the noises and interactions in her environment in the womb, we probably hear our first sounds before we take our first breath. Fairly early in our development, we target in our babbling those sounds that form the phonology of our language or dialect. In interaction with us, our mother adjusts her speech to reflect the phonology, morphology, semantic and syntactic relationships that we are learning. Indeed, our vernacular speech forms the very basis of all future linguistic interaction and development. Across our lifetime we will participate in, construct, engage in, and possibly abandon many speech communities. No other will be as primary.

Identity and Vernacular Connection

How are identity and vernacular connected?

Our vernacular speech is the language of this earliest communication. Through this community, we are introduced to our culture, our heritage, and the ways of being that are important in our development as a member of the human community. It forms the basis of our adult identity. That is why vernacular speech is often called our mother tongue. It is the form of speech spoken to us by our mothers, and it is the mother of (the basis of) the development of other forms of speech.

Our next speech community involves our neighborhood and the larger extended family. Unless we were reared in multi or bilingual communities and neighborhoods, the norms of our vernacular speech community and other early speech communities are not that different from each other. In fact, the first contrast probably occurs when we begin to participate in religion or school. Both of these communities involve regular, face-to-face interaction between us and a larger group of people who may or may not share vernacular speech similar to our own.

Home and School

What may be sources of a conflict in one’s identity, particularly when children enter school?

When we enter school we bring more than the pronunciation patterns, lexicon, syntactic structures, semantic and interpretive frameworks of the language variation or dialect we speak. We have begun to learn to whom we should say what and when. Furthermore, we have learned rules of conversation and linguistic interaction. We have learned to identify whose turn it is to speak, how to get the floor ourselves, and when a person’s turn is over. All of these linguistic skills support us in our first steps toward the development of literacy. When the patterns of the speech communities we join at school are not that much different from the discourse patterns of the speech community (or communities) we participate in with our parents, literary development is more natural and easier.

When the linguistic heritage we bring to school contrasts sharply with the norms of the speech community of the school, it creates difficulties not just for speaking but for participating. If our linguistic heritage is viewed as problematic, divergent, or substandard, we may think of ourselves as problems. We may feel shame for who we are and the community we come from. If how we speak, gain access to participation, interpret behavior, or respond politely is misunderstood by the school as laziness, recalcitrance, disrespectfulness, or stupidity, our entire educational future and our ability to achieve our intellectual potential may be called into question.

Linguistic heritage that is suspect usually comes from those who either speak a different language or use dialects judged to be non-standard. John Ogbu points out that just because people speak a different language or dialect does not mean they will not do well in learning a new language and in achieving success in a new culture. But in the United States as well as other countries in the world, some groups do better in this process than others. Some point to cultural patterns to account for differences in successful participation. Yet, as we look at different immigrant groups we find this may or may not be true. For example, people often suggest that the reason Puerto Ricans have not done well in American schools is because of differences in eye contact in their culture compared to the dominant or majority culture of the United States. However, the Punjabi usually do very well in the United States even though they share similar cultural differences involving eye contact. Furthermore, immigrant Korean and Japanese students do equally well in the majority culture of the U.S., even though their cultural practices are quite different from ours. Ironically, Korean students in Japan whose families were brought there as forced labor do significantly worse than Japanese students, even though the cultures of Korea and Japan are much more alike than the cultures of the U.S. and Korea.

Different Reactions

How do voluntary vs. involuntary groups react?

John Ogbu accounts for these discrepancies by pointing to the difference between voluntary and involuntary (or caste minority) groups. Voluntary immigrants are those who came willingly to this country. They expect to learn a new language and find ways to gain access to and participate in a new culture. Therefore, while the speech community they meet at school is different from their own, they expect to be able to use the vernacular speech they brought with them as the basis from which their new language will emerge. For voluntary immigrants, learning the new language and participating in this new speech community is, in the long run, viewed as a positive and exhilarating experience. Voluntary immigrants plan to add this new language and culture to their repertoire of language styles. They expect to participate in additive bilingualism.

Involuntary immigrants (caste minorities) come into a country against their will, or they represent caste minorities like the forced labor Koreans in Japan and the African Americans in this country. They are also represented by groups of conquered and oppressed people within a country, like Native American groups. In the foundations course we discussed resistance theory. After consistently experiencing rejection by the majority community, students sometimes become aggressive or belligerent toward or actively resist the majority culture. Involuntary immigrants respond in similar ways. Because of the response to their culture and language, these immigrants have developed cultural practices which have emerged either in response to their rejection by the majority culture or have been interpreted by the minority group as resistance toward the majority culture. In order to become part of the majority discourse community, involuntary immigrants feel they will have to give up their own culture and practice subtractive bilingualism. However, when individual members of the community have rejected the language of their speech community, this has not guaranteed their success in the dominant culture.

Language Choice

How do identity and culture formation/crisis translate to an individual’s choice of language(s) and attitudes toward culture(s)?

Involuntary immigrants and caste minorities may feel that they have to give up their vernacular, a vital and central part of their identity, to participate in the speech community of the schools. Caste minorities usually have a shared heritage of rejection by majority institutions. Parents and grandparents may have tried to participate in the discourse of schools and been rejected by those institutions. Therefore, while they recognize the power of the majority culture, they may not be willing or able to support their children in engaging successfully with school speech communities.

Most of us either now or in the past have participated in many speech communities. There is the speech community of our religious affiliations, our occupations or careers, our neighborhood, and our families. As adolescents, we all learned a new language consisting of the register of adolescents. We developed new words for old concepts. We shared language and interaction patterns with our peers that marked us as distinct from the adult culture we would one day join. What we learned as we participated in speech communities was that just because we use the correct language, have the right clothes, and use the right moves, doesn’t mean we will be able to command the discourse of the speech community. In fact, we might still be rejected by it.

When we don’t feel comfortable in a particular speech community, we may adopt strategies like silence, avoidance, or other social practices that protect us from what we perceive may result in public rejection.

Usually, we move easily and fluidly from one speech community to another. We are in the situation—the family reunion, the class reunion, the Sunday School class, and the grocery store—and we simply use the language and social interaction patterns that come to us. Only when we are uncertain of our ability to reproduce the linguistic and social norms appropriate for the community do we become uncomfortable. William Labov pointed out that teachers usually come from white lower-middle-class backgrounds. In the process of becoming educated as teachers, they have crossed the boundary into a different class. As a result, they are hypersensitive to the norms that govern academic discourse communities. This hypersensitivity results in their hypercorrection of the speech of their own students.

Classroom Rules and Power Relations

How do they influence classroom interactions and academic achievement?

As teachers, we often forget that we have authority and power in our classrooms. For the most part, we can decide what kinds of social interaction and linguistic styles, registers and patterns will be acceptable in our classrooms. We can create classrooms that have rigid performance standards rather than classrooms that are places for students to gain experience in learning the culture and linguistic practices of academic discourse. Instead of creating a speech community where all are not just welcomed, but the culture and language they bring is valued and respected, we create communities of exclusion. Research shows that children who can command participation in the widest range of academic discourse communities are those who will be most successful. It also shows that the best predictor of academic achievement is the home background of the child. What this implies is that for most children, schools are not environments for learning and developing skill and potential but a great sorting ground where they are constantly tested and evaluated on what they already know and can do.

Teachers can also be so non-judgmental and inclusive they send false messages to their students. My own daughter once said to me, “My teacher says it doesn’t matter if I can add and subtract. What matters is that I feel good about what I can do.” While I want my daughter to have a strong self-concept, it does indeed matter for her long-term success whether she can command the language of mathematics with precision and accuracy. Thus, as Lisa Delpit points out, teachers may disenfranchise students if they pretend that whatever they bring is enough and if they do not help students from minority and culturally diverse backgrounds to position themselves to command the academic discourses that lead to future success.

Impact on Identity and Development

How do classroom rules impact our students, their identity, and their development?

Teachers need to create classroom communities that not only embrace diversity, but school students in discourse practices allowing and encouraging them to become bidialectal, bilingual, and bicultural and support their identity. We can do this by teaching students the rules of speech communities, by helping them understand differences in the vernacular language they bring to schools and the language of schools—not in ways that discredit their language and cultural heritage but in ways that support its value and complexity.

Teachers belong to many speech communities. More importantly, every year they have the opportunity to co-construct a new speech community. Each year they have a new opportunity to engage students in ways that guarantee them ongoing access to their vernacular speech and primary culture and also give them access to the majority discourse of schools.

Christian D. (1994). Vernacular dialects and standard American English in the classroom [annotated bibliography]. Washington D.C.: ERIC Clearinghouse on Languages and Linguistics.

Clandinin, D.J. & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. San Francisco: Jossey-Bass Publishers.

Duranti, A. (1997). Linguistic anthropology. Cambridge, England: Cambridge University Press.

Gleason, J.B. (1997). The development of language, 4th Ed . Boston: Allyn and Bacon.

Hawkins, M.R. (1997). Positioning, power, and the construction of knowledge in groupwork in a graduate second language teacher education course. Unpublished Dissertation University of Massachusetts Amherst.

Labov, W. (1969). A study of non-standard English. Washington, DC: ERIC Clearinghouse for Linguistics.

Linde, C. (1993). Life stories: The creation of coherence. New York: Oxford University Press.

Ogbu, J. (1995). Understanding cultural diversity and learning (pp. 582–593). In J.A. Banks and C.A.M. Banks (Eds.) Handbook of Multicultural Education. New York: McMillan Publishing, USA.

Ogbu, J. (1999). Beyond language: Ebonics, proper English, and identity in a Black-American speech community. American Educational Research Journal, 36 (2), 147–84.

Owens, R. E., Jr. (2001). Language development: An introduction, 5th Ed . Boston: Allyn and Bacon.

Shaffer, D.R. (1999). Developmental psychology: Childhood and adolescence, 5th Ed. Pacific Grove, CA: Brooks/Cole Publishing.

Wolfram, W., Adger, C. T. , & Christian, D. (1999). Dialects in schools and communities. Mawah, NJ: Lawrence Earlbaum Associates, Publishers.

Adapted with permission from:                                                                                              

Teemant, A. & Pinnegar, S. (2007). Understanding Langauge Acquisition Instructional Guide. Brigham Young University-Public School Partnership. 

Indiana University/Purdue University, Indianapolis (IUPUI)

Brigham Young University

This content is provided to you freely by BYU Open Learning Network.

Access it online or download it at https://open.byu.edu/language_acquisition/speech_communities .

A Definition of Speech Community in Sociolinguistics

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• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

Speech community is a term in sociolinguistics and linguistic anthropology used to describe a group of people who share the same language,  speech  characteristics, and ways of interpreting communication. Speech communities may be large regions like an urban area with a common, distinct accent (think of Boston with its dropped r's) or small units like families and friends (think of a nickname for a sibling). They help people define themselves as individuals and community members and identify (or misidentify) others.

Speech and Identity

The concept of speech as a means of identifying with a community first emerged in 1960s academia alongside other new fields of research like ethnic and gender studies. Linguists like John Gumperz pioneered research in how personal interaction can influence ways of speaking and interpreting, while Noam Chomsky studied how people interpret language and derive meaning from what they see and hear.

Types of Communities

Speech communities can be large or small, although linguists don't agree on how they're defined. Some, like linguist Muriel Saville-Troike, argue that it's logical to assume that a shared language like English, which is spoken throughout the world, is a speech community. But she differentiates between "hard-shelled" communities, which tend to be insular and intimate, like a family or religious sect, and "soft-shelled" communities where there is a lot of interaction.

But other linguists say a common language is too vague to be considered a true speech community. The linguistic anthropologist Zdenek Salzmann describes it this way:

"[P]eople who speak the same language are not always members of the same speech community. On the one hand, speakers of South Asian English in India and Pakistan share a language with citizens of the U.S., but the respective varieties of English and the rules for speaking them are sufficiently distinct to assign the two populations to different speech communities..."

Instead, Salzman and others say, speech communities should be more narrowly defined based on characteristics such as pronunciation, grammar, vocabulary, and manner of speaking.

Study and Research

The concept of speech community plays a role in a number of social science, namely sociology, anthropology, linguists, even psychology. People who study issues of migration and ethnic identity use social community theory to study things like how immigrants assimilate into larger societies, for instance. Academics who focus on racial, ethnic, sexual​ or gender issues apply social community theory when they study issues of personal identity and politics. It also plays a role in data collection. By being aware of how communities are defined, researchers can adjust their subject pools in order to obtain representative sample populations.

• Morgan, Marcyliena H. "What Are Speech Communities?" Cambridge University Press, 2014.
• Salzmann, Zdenek. "Language, Culture, and Society: An Introduction to Linguistic Anthropology." Westview, 2004
• Saville-Troike, Muriel. "The Ethnography of Communication: An Introduction, 3rd ed." Blackwell, 2003.
• The Differences Between Socialism and Communism
• The Difference Between a Speech and Discourse Community
• What Is Linguistic Anthropology?
• Diglossia in Sociolinguistics
• Style-shifting (language)
• Discourse Domain
• Descriptivism in Language
• Communicative Competence Definition, Examples, and Glossary
• Idiolect (Language)
• Linguistic Variation
• Understanding Dialectology
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speech community

Definition of speech community

Examples of speech community in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'speech community.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1894, in the meaning defined above

Dictionary Entries Near speech community

speechcraft

Cite this Entry

“Speech community.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/speech%20community. Accessed 6 Jun. 2024.

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Literary English

Speech Communities in Sociolinguistics | Linguistics

In the vast realm of sociolinguistics, one captivating concept that stands out is “Speech Communities.” These communities form the basis of human language interactions, shaping the way we communicate and connect with one another. In this article, we will delve into the intriguing world of speech communities, exploring their definitions, intersections, and the concept of a community of practice.

What are Speech Communities?

A speech community refers to a group of people who share a common language or dialect and use it to communicate with one another regularly. It is essential to note that speech communities are not solely defined by geographical boundaries but can transcend physical distances through shared linguistic characteristics. In the social context, we expect certain individuals to exhibit linguistic behaviors similar to others, signifying that they may use the same language, dialect, or variety, and thus belong to the same speech community.

The term “speech community” originates from the German Sprachgemeinschaft. Another way to define it is as a group of people who share linguistic norms and expectations concerning language usage. However, there are some ambiguities surrounding this term, and its precise definition remains a topic of debate in scholarly literature.

When defining speech communities, certain aspects are often emphasized, including shared community membership and shared linguistic communication. As sociolinguistics examines language use within or among groups of speakers, the notion of a “group” becomes crucial. Defining a group can be challenging, so instead, we can consider its characteristics:

• A group must consist of at least two members, with no upper limit.
• People can come together in groups for various reasons, such as social, religious, political, cultural, familial, vocational, or avocational purposes.
• Groups may extend beyond their members as individuals can join or leave.
• Group members might also belong to other groups, and they may or may not have face-to-face interactions.
• The organization of a group can be either tight or loose.

Lyons (1970) provides a definition of a ‘real’ speech community as “all the people who use a given language (or dialect).” However, this definition raises questions about how to define a language or dialect, making it synonymous with the definition of a speech community. Thus, a speech community is essentially a social group with distinct speech characteristics that are of interest and can be described coherently.

Definitions of Speech Communities

Throughout the history of sociolinguistics, several distinguished linguists have offered their perspectives on speech communities:

•   Bloomfield (1933) offers a definition of a speech community as “a group of people who communicate through speech.”
• Charles Hockett, a prominent linguist, defined a speech community as a group of people who share a set of rules for communication. These rules encompass both verbal and non-verbal elements, enabling effective understanding and interaction within the community.
• Gumperz (1971) describes a speech community as “any gathering of individuals who regularly and frequently interact using a common set of spoken symbols, and they are distinguished from similar gatherings by notable differences in language usage”.
• William Labov, another influential figure in the field, emphasized the role of social factors in defining speech communities. According to Labov, members of a speech community not only share linguistic features but also adhere to specific norms and attitudes towards language usage.
• As stated by Patrick (2002), the category of group that sociolinguists have typically endeavored to investigate is referred to as the speech community.
• Dell Hymes introduced the concept of “communicative competence” as a defining characteristic of speech communities. This notion emphasizes the ability of community members to understand and produce language appropriately within various social contexts.

Intersecting Communities

Speech communities often intersect, leading to intriguing linguistic phenomena. In areas where multiple speech communities coexist, language contact can result in dialectal variations, code-switching, and language borrowing.

1. Multilingual Communities

In regions with diverse language groups, multilingual communities emerge. These communities foster a rich linguistic environment, where individuals might be proficient in multiple languages and engage in code-switching effortlessly.

2. Urban vs. Rural Communities

Urban centers, characterized by greater diversity and mobility, often house a variety of speech communities. On the other hand, rural areas might have more homogeneous speech communities with distinct linguistic features.

The Idea of Community of Practice

The concept of a “community of practice” complements the notion of speech communities by focusing on shared activities and interests. A community of practice involves individuals who come together to pursue a common goal, develop their skills, and create a shared language related to their domain. For example, within a workplace, employees involved in a specific project form a community of practice, exchanging domain-specific jargon and expressions that outsiders may find difficult to understand.

Speech communities play a fundamental role in shaping our linguistic identities and social interactions. From the definitions proposed by renowned linguists to the fascinating intersections with other communities, the study of speech communities provides valuable insights into the intricate world of sociolinguistics. Understanding the dynamics of speech communities can lead to enhanced cross-cultural communication, better appreciation of linguistic diversity, and the promotion of inclusive practices in various social settings.

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Remarks by Secretary of Defense Lloyd J. Austin III Commemorating 80 Years Since D-Day (As Delivered)

President Biden, Dr. Biden, President Macron, Mrs. Macron, distinguished guests, ladies and gentlemen, and above all, the veterans of World War II: I am honored to stand again at this hallowed place. 

We bow our heads to remember the more than 9,000 U.S. and Allied soldiers killed or wounded on D-Day by Hitler's forces.   On behalf of the United States Department of Defense, I am here to give thanks—inadequate as that word may be.    Eighty years later, we thank the young Americans who took the beaches, who helped liberate France, and who helped free this continent from Nazi tyranny. We thank every Allied warrior who fought for freedom on June 6, 1944. And we thank the American and Allied veterans who have rallied once more on the shores of Normandy.   Victors of D-Day: we are humbled by your presence.   The young Americans who fought through the clamor and the chaos on D-Day have grown old, or left us. And whenever a veteran of D-Day is gathered to his Maker in the fullness of time, after a long life lived in freedom, he wins a final victory over Hitler.    You helped defeat what Churchill called "a monstrous tyranny, never surpassed in the dark, lamentable catalogue of human crime." And you laid the foundation for a more just, free, and decent world. 

Together with our Allies, we built peace out of war—a hard-headed peace, a peace renewed by constant commitment, a peace forged by the generation that won the bloodiest war in human history.    And so our gratitude must never fade. Our memories must never dim. And our resolve must never fail.    We still seek a world where aggression is a sin, and where human rights are sacred, and where all people can live in freedom.    And so we must rally again to defend the open, postwar world of rules, rights, and responsibilities. 

Those rules protect us. Those rights define us. And those responsibilities summon us once more.    At this hinge in history, we must again stand firm against aggression and tyranny. 

And as I said here last year: If the troops of the world's democracies could risk their lives for freedom then, surely the citizens of the world's democracies can risk our comfort for freedom now.    So let us again uphold the spirit of D-Day. Let us again defend the principles that the Allied armies carried. And let us again thank the heroes of D-Day who kept freedom alive for us all.    [Applause]   You saved the world.   [Applause]   You saved the world. And we must only defend it. 

Gentlemen: we salute you.   [Secretary Austin turns and salutes the World War II veterans; audience applauds]   May God bless the American and Allied troops who fought here. May God bless the United States of America. And may God bless all who cherish human freedom.   Thank you.     [Applause]

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• Childhood apraxia of speech

Childhood apraxia of speech (CAS) is a rare speech disorder. Children with this disorder have trouble controlling their lips, jaws and tongues when speaking.

In CAS , the brain has trouble planning for speech movement. The brain isn't able to properly direct the movements needed for speech. The speech muscles aren't weak, but the muscles don't form words the right way.

To speak correctly, the brain has to make plans that tell the speech muscles how to move the lips, jaw and tongue. The movements usually result in accurate sounds and words spoken at the proper speed and rhythm. CAS affects this process.

CAS is often treated with speech therapy. During speech therapy, a speech-language pathologist teaches the child to practice the correct way to say words, syllables and phrases.

Children with childhood apraxia of speech (CAS) may have a variety of speech symptoms. Symptoms vary depending on a child's age and the severity of the speech problems.

CAS can result in:

• Babbling less or making fewer vocal sounds than is typical between the ages of 7 to 12 months.
• Speaking first words late, typically after ages 12 to 18 months old.
• Using a limited number of consonants and vowels.
• Often leaving out sounds when speaking.
• Using speech that is hard to understand.

These symptoms are usually noticed between ages 18 months and 2 years. Symptoms at this age may indicate suspected CAS . Suspected CAS means a child may potentially have this speech disorder. The child's speech development should be watched to determine if therapy should begin.

Children usually produce more speech between ages 2 and 4. Signs that may indicate CAS include:

• Vowel and consonant distortions.
• Pauses between syllables or words.
• Voicing errors, such as "pie" sounding like "bye."

Many children with CAS have trouble getting their jaws, lips and tongues to the correct positions to make a sound. They also may have a hard time moving smoothly to the next sound.

Many children with CAS also have language problems, such as reduced vocabulary or trouble with word order.

Some symptoms may be unique to children with CAS , which helps to make a diagnosis. However, some symptoms of CAS are also symptoms of other types of speech or language disorders. It's hard to diagnose CAS if a child has only symptoms that are found both in CAS and in other disorders.

Some characteristics, sometimes called markers, help distinguish CAS from other types of speech disorders. Those associated with CAS include:

• Trouble moving smoothly from one sound, syllable or word to another.
• Groping movements with the jaw, lips or tongue to try to make the correct movement for speech sounds.
• Vowel distortions, such as trying to use the correct vowel but saying it incorrectly.
• Using the wrong stress in a word, such as pronouncing "banana" as "BUH-nan-uh" instead of "buh-NAN-uh."
• Using equal emphasis on all syllables, such as saying "BUH-NAN-UH."
• Separation of syllables, such as putting a pause or gap between syllables.
• Inconsistency, such as making different errors when trying to say the same word a second time.
• Having a hard time imitating simple words.
• Voicing errors, such as saying "down" instead of "town."

Other speech disorders sometimes confused with CAS

Some speech sound disorders often get confused with CAS because some of the symptoms may overlap. These speech sound disorders include articulation disorders, phonological disorders and dysarthria.

A child with an articulation or phonological disorder has trouble learning how to make and use specific sounds. Unlike in CAS , the child doesn't have trouble planning or coordinating the movements to speak. Articulation and phonological disorders are more common than CAS .

Articulation or phonological speech errors may include:

• Substituting sounds. The child might say "fum" instead of "thumb," "wabbit" instead of "rabbit" or "tup" instead of "cup."
• Leaving out final consonants. A child with CAS might say "duh" instead of "duck" or "uh" instead of "up."
• Stopping the airstream. The child might say "tun" instead of "sun" or "doo" instead of "zoo."
• Simplifying sound combinations. The child might say "ting" instead of "string" or "fog" instead of "frog."

Dysarthria is a speech disorder that occurs because the speech muscles are weak. Making speech sounds is hard because the speech muscles can't move as far, as quickly or as strongly as they do during typical speech. People with dysarthria may also have a hoarse, soft or even strained voice. Or they may have slurred or slow speech.

Dysarthria is often easier to identify than CAS . However, when dysarthria is caused by damage to areas of the brain that affect coordination, it can be hard to determine the differences between CAS and dysarthria.

Childhood apraxia of speech (CAS) has a number of possible causes. But often a cause can't be determined. There usually isn't an observable problem in the brain of a child with CAS .

However, CAS can be the result of brain conditions or injury. These may include a stroke, infections or traumatic brain injury.

CAS also may occur as a symptom of a genetic disorder, syndrome or metabolic condition.

CAS is sometimes referred to as developmental apraxia. But children with CAS don't make typical developmental sound errors and they don't grow out of CAS . This is unlike children with delayed speech or developmental disorders who typically follow patterns in speech and sounds development but at a slower pace than usual.

Risk factors

Changes in the FOXP2 gene appear to increase the risk of childhood apraxia of speech (CAS) and other speech and language disorders. The FOXP2 gene may be involved in how certain nerves and pathways in the brain develop. Researchers continue to study how changes in the FOXP2 gene may affect motor coordination and speech and language processing in the brain. Other genes also may impact motor speech development.

Complications

Many children with childhood apraxia of speech (CAS) have other problems that affect their ability to communicate. These problems aren't due to CAS , but they may be seen along with CAS .

Symptoms or problems that are often present along with CAS include:

• Delayed language. This may include trouble understanding speech, reduced vocabulary, or not using correct grammar when putting words together in a phrase or sentence.
• Delays in intellectual and motor development and problems with reading, spelling and writing.
• Trouble with gross and fine motor movement skills or coordination.
• Trouble using communication in social interactions.

Diagnosing and treating childhood apraxia of speech at an early stage may reduce the risk of long-term persistence of the problem. If your child experiences speech problems, have a speech-language pathologist evaluate your child as soon as you notice any speech problems.

Childhood apraxia of speech care at Mayo Clinic

• Jankovic J, et al., eds. Dysarthria and apraxia of speech. In: Bradley and Daroff's Neurology in Clinical Practice. 8th ed. Elsevier; 2022. https://www.clinicalkey.com. Accessed April 6, 2023.
• Carter J, et al. Etiology of speech and language disorders in children. https://www.uptodate.com/contents/search. Accessed April 6, 2023.
• Childhood apraxia of speech. American Speech-Language-Hearing Association. https://www.asha.org/public/speech/disorders/childhood-apraxia-of-speech/. Accessed April 6, 2023.
• Apraxia of speech. National Institute on Deafness and Other Communication Disorders. http://www.nidcd.nih.gov/health/voice/pages/apraxia.aspx. Accessed April 6, 2023.
• Ng WL, et al. Predicting treatment of outcomes in rapid syllable transition treatment: An individual participant data meta-analysis. Journal of Speech, Language and Hearing Research. 2022; doi:10.1044/2022_JSLHR-21-00617.
• Speech sound disorders. American Speech-Language-Hearing Association. http://www.asha.org/public/speech/disorders/SpeechSoundDisorders/. Accessed April 6, 2023.
• Iuzzini-Seigel J. Prologue to the forum: Care of the whole child — Key considerations when working with children with childhood apraxia of speech. Language, Speech and Hearing Services in Schools. 2022; doi:10.1044/2022_LSHSS-22-00119.
• Namasivayam AK, et al. Speech sound disorders in children: An articulatory phonology perspective. 2020; doi:10.3389/fpsyg.2019.02998.
• Strand EA. Dynamic temporal and tactile cueing: A treatment strategy for childhood apraxia of speech. American Journal of Speech-Language Pathology. 2020; doi:10.1044/2019_AJSLP-19-0005.
• Ami TR. Allscripts EPSi. Mayo Clinic. March 13, 2023.
• Kliegman RM, et al. Language development and communication disorders. In: Nelson Textbook of Pediatrics. 21st ed. Elsevier; 2020. https://www.clinicalkey.com. Accessed April 6, 2023.
• Adam MP, et al., eds. FOXP2-related speech and language disorder. In: GeneReviews. University of Washington, Seattle; 1993-2023. https://www.ncbi.nlm.nih.gov/books/NBK1116. Accessed April 6, 2023.
• How is CAS diagnosed? Childhood Apraxia of Speech Association of North America. https://www.apraxia-kids.org/apraxia_kids_library/how-is-cas-diagnosed/. Accessed April 13, 2023.
• Chenausky KV, et al. The importance of deep speech phenotyping for neurodevelopmental and genetic disorders: A conceptual review. Journal of Neurodevelopmental Disorders. 2022; doi:10.1186/s11689-022-09443-z.
• Strand EA. Dynamic temporal and tactile cueing: A treatment strategy for childhood apraxia of speech. American Journal of Speech Language Pathology. 2020; doi:10.1044/2019_AJSLP-19-0005.
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A Beginner’s Guide to Language Models

A language model is a probability distribution over words or word sequences. Learn more about different types of language models and what they can do.

Extracting information from textual data has changed dramatically over the past decade. As the term natural language processing has overtaken text mining as the name of the field, the methodology has changed tremendously, too. One of the main drivers of this change was the emergence of language models as a basis for many applications aiming to distill valuable insights from raw text.

Language Model Definition

In learning about natural language processing, I’ve been fascinated by the evolution of language models over the past years. You may have heard about GPT-3 and the potential threats it poses , but how did we get this far? How can a machine produce an article that mimics a journalist?

What Is a Language Model?

A language model is a probability distribution over words or word sequences. In practice, it gives the probability of a certain word sequence being “valid.” Validity in this context does not refer to grammatical validity. Instead, it means that it resembles how people write, which is what the language model learns. This is an important point. There’s no magic to a language model like other machine learning models , particularly deep neural networks , it’s just a tool to incorporate abundant information in a concise manner that’s reusable in an out-of-sample context.

More on Data Science: Basic Probability Theory and Statistics Terms to Know 

What Can a Language Model Do?

The abstract understanding of natural language, which is necessary to infer word probabilities from context, can be used for a number of tasks. Lemmatization or stemming aims to reduce a word to its most basic form, thereby dramatically decreasing the number of tokens. These algorithms work better if the part-of-speech role of the word is known. A verb’s postfixes can be different from a noun’s postfixes, hence the rationale for part-of-speech tagging (or POS-tagging ), a common task for a language model.

With a good language model, we can perform extractive or abstractive summarization of texts. If we have models for different languages, a machine translation system can be built easily. Less straightforward use-cases include answering questions (with or without context, see the example at the end of the article). Language models can also be used for speech recognition , OCR , handwriting recognition and more. There’s a whole spectrum of opportunities.

Types of Language Models

There are two types of language models: 

• Probabilistic methods.
• Neural network-based modern language models

It’s important to note the difference between them.

Probabilistic Language Model

A simple probabilistic language model is constructed by calculating n-gram probabilities. An n-gram is an n word sequence, n being an integer greater than zero. An n-gram’s probability is the conditional probability that the n-gram’s last word follows a particular n-1 gram (leaving out the last word). It’s the proportion of occurrences of the last word following the n-1 gram leaving the last word out. This concept is a Markov assumption. Given the n-1 gram (the present), the n-gram probabilities (future) does not depend on the n-2, n-3, etc grams (past).

There are evident drawbacks of this approach. Most importantly, only the preceding n words affect the probability distribution of the next word. Complicated texts have deep context that may have decisive influence on the choice of the next word. Thus, what the next word is might not be evident from the previous n-words, not even if n is 20 or 50. A term has influence on a previous word choice: the word United is much more probable if it is followed by States of America. Let’s call this the context problem.

On top of that, it’s evident that this approach scales poorly. As size increases (n), the number of possible permutations skyrocket, even though most of the permutations never occur in the text. And all the occuring probabilities (or all n-gram counts) have to be calculated and stored. In addition, non-occurring n-grams create a sparsity problem , as in, the granularity of the probability distribution can be quite low. Word probabilities have few different values, therefore most of the words have the same probability.

Neural Network-Based Language Models

Neural network based language models ease the sparsity problem by the way they encode inputs. Word embedding layers create an arbitrary sized vector of each word that incorporates semantic relationships as well. These continuous vectors create the much needed granularity in the probability distribution of the next word. Moreover, the language model is a function, as all neural networks are with lots of matrix computations, so it’s not necessary to store all n-gram counts to produce the probability distribution of the next word.

Evolution of Language Models

Even though neural networks solve the sparsity problem, the context problem remains. First, language models were developed to solve the context problem more and more efficiently — bringing more and more context words to influence the probability distribution. Secondly, the goal was to create an architecture that gives the model the ability to learn which context words are more important than others.

The first model, which I outlined previously, is a dense (or hidden) layer and an output layer stacked on top of a continuous bag-of-words (CBOW) Word2Vec model . A CBOW Word2Vec model is trained to guess the word from context. A Skip-Gram Word2Vec model does the opposite, guessing context from the word. In practice, a CBOW Word2Vec model requires a lot of examples of the following structure to train it: the inputs are n words before and/or after the word, which is the output. We can see that the context problem is still intact.

Recurrent Neural Networks (RNN)

Recurrent neural networks (RNNs) are an improvement regarding this matter. Since RNNs can be either a long short-term memory (LSTM) or a gated recurrent unit (GRU) cell based network, they take all previous words into account when choosing the next word. AllenNLP’s ELMo takes this notion a step further, utilizing a bidirectional LSTM, which takes into account the context before and after the word counts.

Transformers

The main drawback of RNN-based architectures stems from their sequential nature. As a consequence, training times soar for long sequences because there is no possibility for parallelization. The solution for this problem is the transformer architecture .

The GPT models from OpenAI and Google’s BERT utilize the transformer architecture, as well. These models also employ a mechanism called “Attention,” by which the model can learn which inputs deserve more attention than others in certain cases.

In terms of model architecture, the main quantum leaps were firstly RNNs, specifically, LSTM and GRU, solving the sparsity problem and reducing the disk space language models use, and subsequently, the transformer architecture, making parallelization possible and creating attention mechanisms. But architecture is not the only aspect a language model can excel in.

Compared to the GPT-1 architecture, GPT-3 has virtually nothing novel. But it’s huge. It has 175 billion parameters, and it was trained on the largest corpus a model has ever been trained on in common crawl. This is partly possible because of the semi-supervised training strategy of a language model. A text can be used as a training example with some words omitted. The incredible power of GPT-3 comes from the fact that it has read more or less all text that has appeared on the internet over the past years, and it has the capability to reflect most of the complexity natural language contains.

Trained for Multiple Purposes

Finally, I’d like to review the T5 model from Google . Previously, language models were used for standard NLP tasks, like part-of-speech (POS) tagging or machine translation with slight modifications. With a little retraining , BERT can be a POS-tagger because of its abstract ability to understand the underlying structure of natural language. 

With T5, there is no need for any modifications for NLP tasks. If it gets a text with some <M> tokens in it, it knows that those tokens are gaps to fill with the appropriate words. It can also answer questions. If it receives some context after the questions, it searches the context for the answer. Otherwise, it answers from its own knowledge. Fun fact: It beat its own creators in a trivia quiz. 

More on Language Models: NLP for Beginners: A Complete Guide

Future of Language Models

Personally, I think this is the field that we are closest to creating an AI. There’s a lot of buzz around AI, and many simple decision systems and almost any neural network are called AI, but this is mainly marketing. By definition, artificial intelligence involves human-like intelligence capabilities performed by a machine. While transfer learning shines in the field of computer vision, and the notion of transfer learning is essential for an AI system, the very fact that the same model can do a wide range of NLP tasks and can infer what to do from the input is itself spectacular. It brings us one step closer to actually creating human-like intelligence systems.

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