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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Prosodic boundary tone classification with voice quality features.

Ran Han¹, Jeung-Yoon Choi

¹School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea. magnolia@dsp.yonsei.ac.kr

The Journal of the Acoustical Society of America

|April 6, 2013

Summary

This summary is machine-generated.

This study shows that voice quality features significantly improve the classification of vocalic segments into boundary tones. Using these features, particularly for low-tone boundaries, results in more accurate prosodic analysis.

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Area of Science:

Phonetics and Speech Science
Computational Linguistics
Acoustic Phonetics

Background:

Accurate prosodic boundary tone classification is crucial for speech understanding.
Traditional features like pitch, duration, and amplitude have limitations in capturing subtle prosodic nuances.
The Tones and Break Indices (TBI) system categorizes prosodic boundaries into five types.

Purpose of the Study:

To investigate the efficacy of voice quality features in classifying vocalic segments into TBI boundary tones.
To compare the performance of voice quality features against traditional acoustic and prosodic features.
To determine the significance of voice quality features, especially for low-tone boundary distinctions.

Main Methods:

Analysis of static and nonstatic voice quality features, including harmonic structure and spectral tilt.
Comparison with features related to segment duration, pitch, amplitude, and adjacent segment characteristics.
Feature selection using statistical tests (Kruskal-Wallis), Akaike Information Criterion (AIC), and sequential forward search.

Main Results:

Voice quality features were found to be statistically significant for classifying prosodic boundary tones.
These features demonstrated particular effectiveness in distinguishing low-tone boundaries.
Classification models utilizing selected voice quality features achieved lower balanced error rates compared to models using other feature sets.

Conclusions:

Voice quality features offer a valuable addition to traditional methods for prosodic boundary tone classification.
Incorporating voice quality metrics enhances the accuracy of the Tones and Break Indices system.
Further research into voice quality for prosodic analysis is warranted.