tone recognition with fractionized models and outlined features
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Tone Recognition With Fractionized Models and Outlined Features. Ye Tian , Jian -Lai Zhou, Min Chu, Eric Chang ICASSP 2004. Hsiao- Tsung Hung. Department of Computer Science and Information Engineering National Taiwan Normal University. Outline. Introduction Features Detailed features

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tone recognition with fractionized models and outlined features

Tone Recognition With Fractionized Models and Outlined Features

Ye Tian, Jian-Lai Zhou, Min Chu, Eric Chang

ICASSP 2004

Hsiao-TsungHung

Department of Computer Science and Information Engineering

National Taiwan Normal University

outline
Outline
  • Introduction
  • Features
    • Detailed features
    • Outlined features
    • Experiments and analysis
  • Tone Modeling
    • Experiments and analysis
  • Conclusions
introduction
Introduction
  • 2 questions
  • Is the detailed information of F0 curve useful for tone discrimination in continuous speech?
  • Are phoneme-independent tone models sufficient for continuous speech recognition?
detailed features
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
Outlined features
  • To reduce the number of parameters and improve the robustness.
  • Curve fitting features
  • Subsection Outlined features
curve fitting features
Curve fitting features
  • First-order
  • Second-order
subsection outlined features
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.)

slide8

Y

F0

X

X={0,1,…,//frame

Y=

subsection outlined features1
Subsection Outlined features

1.subsection slop and intercept

experiments and analysis
Experiments and analysis

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

2.Detailed information is useless for tone discrimination.

tone modeling
Tone Modeling
  • One-tone-one-model tone models(5)
  • Monophone-dependent tone models(54)

The same tone in different tonal phonemes is different modeled.

  • Triphone-dependent tone models(12824)
experiments and analysis1
Experiments and analysis
  • Feature vector :
conclusions
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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