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Related Concept Videos

Language Development01:22

Language Development

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...
Components of Language01:24

Components of Language

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. “eh”). Phonemes combine to...

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Related Experiment Video

Updated: Jun 28, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

A multidialect multidomain Tibetan speech dataset for speech and language processing.

Chao Wang1,2, Yuqing Cai2,3, Renzeng Duojie4,5

  • 1Qinghai Normal University, Xining, China.

Scientific Data
|June 26, 2026
PubMed
Summary

A new Tibetan speech corpus was created, featuring three dialects and three domains. This resource supports research in low-resource speech technology and linguistic diversity preservation.

Related Experiment Videos

Last Updated: Jun 28, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Area of Science:

  • Linguistics
  • Computational Linguistics
  • Speech Technology

Background:

  • Tibetan language is morphologically complex with significant dialectal variation.
  • High-quality, open-access Tibetan speech resources are scarce.
  • Existing resources do not adequately cover diverse dialects and application domains.

Purpose of the Study:

  • To develop and release a comprehensive, multi-dialect, multi-domain Tibetan speech corpus.
  • To address the critical gap in publicly available Tibetan speech data.
  • To facilitate research in low-resource speech technologies and Tibetan linguistic diversity.

Main Methods:

  • Construction of a speech corpus covering three major Tibetan dialects (Ü-Tsang, Amdo, Kham).
  • Inclusion of data from three application domains: general, medical, and financial.
  • Collection of approximately 20.39 hours of speech from 13 native speakers with verified transcriptions and metadata.
  • Conducting baseline experiments for dialect identification and domain classification.

Main Results:

  • The corpus demonstrates stable acoustic feature distributions across dialects.
  • Consistent cross-domain modeling performance was observed.
  • Baseline experiments confirmed the dataset's internal consistency and reproducibility.
  • The corpus contains 20.39 hours of speech data with verified transcriptions.

Conclusions:

  • The newly developed Tibetan speech corpus fills a significant gap in available resources.
  • The dataset provides a valuable foundation for advancing low-resource speech technology research.
  • This resource supports efforts in preserving Tibetan linguistic diversity.
  • The corpus is suitable for dialect identification and domain classification research.