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Mandarin tone recognition based on wavelet transform and hidden Markov modeling

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Journal of Electronics (China)

Abstract

This paper presents a method of tone recognition for Mandarin speech by using combination of wavelet transform and hidden Markov modeling techniques. A pitch detector based on singularity detection and multi-resolution analysis of wavelet transform is employed for estimation of pitch periods, and hidden Markov modeling with partition Gaussian mixtures probability density function is used for the tone recognition. The algorithm can provide recognition accuracy of 97.22% and 94.47% for speaker-dependent and speaker-independent tone recognition, respectively.

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Authors and Affiliations

  1. National Key Laboratory on ISN, Xidian University, 710071, Xi’an

    Cheng Jun, Yi Kechu & Li Bingbing

Authors
  1. Cheng Jun
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  2. Yi Kechu
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  3. Li Bingbing
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Additional information

Supported by the National Natural Science Foundation of China

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Cheng, J., Yi, K. & Li, B. Mandarin tone recognition based on wavelet transform and hidden Markov modeling. J. of Electron.(China) 17, 1–8 (2000). https://doi.org/10.1007/s11767-000-0015-y

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  • DOI: https://doi.org/10.1007/s11767-000-0015-y

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