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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- 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

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  1. 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

  2. Outline • Introduction • Features • Detailed features • Outlined features • Experiments and analysis • Tone Modeling • Experiments and analysis • Conclusions

  3. 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?

  4. 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.

  5. Outlined features • To reduce the number of parameters and improve the robustness. • Curve fitting features • Subsection Outlined features

  6. Curve fitting features • First-order • Second-order

  7. 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.)

  8. Y F0 X X={0,1,…,//frame Y=

  9. Subsection Outlined features 1.subsection slop and intercept

  10. Subsection Outlined features 2.subsection and

  11. Experiments and analysis 1.Main value and direction are the most important characteristics. 2.Detailed information is useless for tone discrimination.

  12. 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)

  13. Experiments and analysis • Feature vector :

  14. 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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