Tone Recognition With Fractionized Models and Outlined Features

transcript

Ye Tian, Jian-Lai Zhou, Min Chu, Eric ChangICASSP 2004

Hsiao-Tsung Hung

Department of Computer Science and Information EngineeringNational Taiwan Normal University

Outline

• Introduction• Features– Detailed features– Outlined features– Experiments and analysis

• Tone Modeling– Experiments and analysis

• Conclusions

Introduction

• 2 questions1. Is the detailed information of F0 curve useful

for tone discrimination in continuous speech?

2. Are phoneme-independent tone models sufficient for continuous speech recognition?

Detailed features

• Detailed features: Using the entire F0 curve.• Observation vector is • If the phoneme has totally N frames, the

number of total parameters used for tone recognition is 2*N.

Outlined features

• To reduce the number of parameters and improve the robustness.

1. Curve fitting features2. Subsection Outlined features

Curve fitting features

• First-order

• Second-order

Subsection Outlined features

• The F0 curve of the entire phoneme is divided into several subsections and each subsection is represented by certain parameters.

• Extract parameters for each subsection– 1.subsection slop and intercept– 2.subsection and (Assume that time frames belong to the subsection k.)

X={0,1,…, //frame

1.subsection slop and intercept

2.subsection and

Experiments and analysis

1.Main value and direction are the most important characteristics.

2.Detailed information is useless for tone discrimination.

Tone Modeling

1. One-tone-one-model tone models(5)2. Monophone-dependent tone models(54)

The same tone in different tonal phonemes is different modeled.

3. Triphone-dependent tone models(12824)

Experiments and analysis

• Feature vector :

Conclusions

• Using fractionized models and outlined features for tone recognition.

• Outlined features can reduce the interference caused by co-articulation effect, syllable stress, and sentence intonation.

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