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Related Concept Videos

Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Dynamic Norming for Systematic Analysis of Language Transcripts.

Alexander Tucci1, Elena Plante1, John J Heilmann2

  • 1Department of Speech, Language, and Hearing Sciences, The University of Arizona, Tucson.

Journal of Speech, Language, and Hearing Research : JSLHR
|December 10, 2021
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Summary
This summary is machine-generated.

This study confirms that smaller, psychometrically stable comparison samples can be created using the Systematic Analysis of Language Transcripts (SALT) dynamic norming system, reducing the need for large normative datasets.

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Area of Science:

  • Speech and Language Pathology
  • Psychometrics
  • Clinical Linguistics

Background:

  • Dynamic norming is a method for creating matched comparison samples in language assessments.
  • The Systematic Analysis of Language Transcripts (SALT) provides large databases for language analysis.
  • Establishing the psychometric stability of dynamic norming with smaller sample sizes is crucial for clinical efficiency.

Purpose of the Study:

  • To determine the psychometric stability of a dynamic norming system utilizing SALT databases.
  • To identify the minimum sample size required for stable results in dynamic norming.
  • To explore the stability of standard error of measure (SEM) for key language transcript metrics.

Main Methods:

  • Two-phase study using English Conversation and Student-Selected Story (SSS) Narrative SALT databases.
  • Phase 1: Exploratory analysis of SEM for six transcript metrics at sampling intervals.
  • Phase 2: Confirmatory analysis using random samples to simulate clinical comparisons and validate Phase 1 findings.

Main Results:

  • Low standard error of measure (SEM) values were observed across both databases.
  • Stable SEM estimations were achieved with sample sizes as small as 40-50 for Conversation and 20-30 for SSS Narrative data.
  • Confirmatory analyses confirmed stable estimations with age bands of ±4 months and approximately 35+ children.

Conclusions:

  • The SALT dynamic norming system can generate psychometrically stable comparison samples efficiently.
  • Smaller sample sizes are sufficient for stable dynamic norming compared to traditional test recommendations.
  • This approach enhances the practicality of language assessment for clinicians.