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

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Unsupervised joint prosody labeling and modeling for Mandarin speech.

Chen-Yu Chiang¹, Sin-Horng Chen, Hsiu-Min Yu

¹Department of Communication Engineering, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China. gene.cm91g@nctu.edu.tw

The Journal of the Acoustical Society of America

|February 12, 2009

Summary

This summary is machine-generated.

This study introduces an unsupervised method for Mandarin speech prosody labeling and modeling. The new approach consistently labels prosodic tags and models speech patterns, outperforming human labelers in consistency.

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Area of Science:

Computational Linguistics
Speech Processing
Phonetics

Background:

Prosody plays a crucial role in speech perception and linguistic analysis.
Accurate prosodic labeling and modeling are essential for understanding speech structure.
Existing methods often lack consistency and discriminative power in prosodic feature analysis.

Purpose of the Study:

To propose an unsupervised joint prosody labeling and modeling method for Mandarin speech.
To develop a consistent scheme for constructing statistical prosodic models and labeling prosodic tags.
To illustrate the hierarchy of Mandarin prosody through a novel modeling approach.

Main Methods:

Developed four prosodic models to represent the hierarchy of Mandarin prosody.
Defined two types of prosodic tags: syllable juncture breaks and prosodic states.
Utilized an unsupervised joint learning approach for labeling and modeling.
Evaluated the method on an unlabeled read-speech corpus.

Main Results:

The four prosodic models successfully explored and described Mandarin prosody structures and patterns.
Identified relationships between labeled break indices and associated words, highlighting prosodic-linguistic connections.
Demonstrated superior consistency and discriminative power in prosodic feature distributions compared to human labelers.

Conclusions:

The proposed unsupervised method provides a consistent and effective approach to Mandarin prosody labeling and modeling.
The findings validate the method's capability in capturing prosodic structures and their relation to linguistic parameters.
This advancement offers significant potential for applications in prosody modeling and speech analysis.