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Modeling Lexical Tones for Speaker Discrimination.

Ricky K W Chan1, Bruce Xiao Wang2

  • 1Speech, Language and Cognition Laboratory, School of English, The University of Hong Kong, Hong Kong.

Language and Speech
|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study compared methods for modeling lexical tone in speech for speaker discrimination. Discrete cosine transform (DCT) and polynomial curve fitting showed promising results for speaker identification, outperforming quantitative target approximation (qTA).

Keywords:
CantoneseMandarinSpeaker discriminationfundamental frequencylexical tone

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Area of Science:

  • Phonetics
  • Speech Science
  • Forensic Linguistics

Background:

  • Fundamental frequency (F0) is crucial for speaker discrimination, but research often overlooks dynamic lexical tone.
  • Existing methods for analyzing tonal F0 lack standardized parameterization for optimal speaker discrimination.

Purpose of the Study:

  • To compare the speaker discriminatory performance of three lexical tone modeling approaches: Discrete Cosine Transform (DCT), polynomial curve fitting, and quantitative target approximation (qTA).
  • To identify the most effective parameterization method for utilizing tonal F0 in speaker identification and forensic voice comparison.

Main Methods:

  • Lexical tone modeling using Discrete Cosine Transform (DCT) for feature extraction.
  • Lexical tone modeling using polynomial curve fitting for feature extraction.
  • Lexical tone modeling using quantitative target approximation (qTA) for feature extraction.
  • Comparative analysis of speaker discriminatory performance across the three modeling techniques.

Main Results:

  • Parameters derived from DCT and polynomial curve fitting demonstrated comparable and promising speaker discriminatory performance.
  • Parameters derived from quantitative target approximation (qTA) generally yielded significantly poorer speaker discrimination results.
  • The study highlights the effectiveness of DCT and polynomial methods in capturing linguistically structured F0 dynamics for speaker identification.

Conclusions:

  • DCT and polynomial curve fitting are effective methods for modeling surface tonal F0 for speaker discrimination.
  • qTA is less suitable for speaker discrimination tasks compared to DCT and polynomial methods.
  • Further research should explore the implications of these findings for understanding articulatory processes in speaker identification.