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Volume 28 Issue 3
Sep.  2010
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Sun Wei, Wu Zhen-yang, Liu Hai-bin. Nonlinear Statistical Matching for Subband Robust Speech Recognition[J]. Journal of Electronics & Information Technology, 2006, 28(3): 480-484.
Citation: Sun Wei, Wu Zhen-yang, Liu Hai-bin. Nonlinear Statistical Matching for Subband Robust Speech Recognition[J]. Journal of Electronics & Information Technology, 2006, 28(3): 480-484.

Nonlinear Statistical Matching for Subband Robust Speech Recognition

  • Received Date: 2004-08-05
  • Rev Recd Date: 2005-04-21
  • Publish Date: 2006-03-19
  • The performance of the speech recognition systems is deteriorated dramatically under noise condition for variation of speech signal. According to the auditory tests, this paper proposes a new nonlinear sub-band Maximum A Posteriori (MAP)statistical matching algorithm based on the independent sub-band analysis. According to the perception of humans ear and noise feature of different frequency-bands, the algorithm compensates the effects of noise with statistical matching, MAP estimation and HMM/MLP nonlinear mapping. The test shows that the proposed algorithm improves the recognition performance notably under noise condition.
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  • Cooke M, Morris A, Green P. Missing data techniques for robust speech recognition[C][J].ICASSP97, Munich, Germany.1997, vol 2:863-[2]Diakoloukas V D, Digalakis V V. Maximum-likelihood stochastic-transformation adaptation of hidden Markov models[J].IEEE Trans. on Speech and Audio Processing.1999, 7(2):177-[3]Siohan O, Chesta C, Lee C -H. Hidden Markov model adaptation using maximum a posteriori linear regression[C]. In Workshop on Robust Methods for Speech Recognition in Adverse Conditions, Tampere, Finland, 1999: 147150. .[4]Gales M, Young S. Cepstral parameter compensation for HMM recognition in noise[J]. Computer Speech and Language, 1993, 12(3):231.239.[5]Sharma S R. Multistream approach to robust speech recognition[D/D]. Oregon Graduate Institute of Science and Technology, 1999.10.[6]Tibrewala S, Hermansky H. Subband based recognition of noisy speech[C][J].ICASSP97, Munich, Germany.1997, vol 2:1255-[7]Ji M, Smith F J. A probabilistic union model for subband based robust speech recognition[C]. ICASSP'00, Istanbul, Turkey, 2000, vol 3: 1787.1790.[8]孙暐, 吴镇扬, 刘海滨等. 并行子带HMM最大后验概率自适应非线性类估计算法[J]. 电路与系统, 录用待刊.[9]Allen J B. How do humans process and recognize speech[J]. IEEE Trans. on Speech and Audio Processing, 1994, 2(4): 567577. .[10]Dempster A P, Laird N M, Rubin D B. Maximum likelihood estimation from incomplete data[J]. J Royal Statistical Society,Serials B, 1977, 39(1): 138. .[11]Ris C, Dupont S. Assessing local noise level estimation methods: application to noise robust ASR[J].Speech Communication.2001, 34(1-2):141-[12]Hirsh H G.. Estimation of noise spectrum and its application to SNR estimation and speech enhancement. Technical ReportTR-93-012, International Computer Science Institute, Berkeley,USA, 1993.[13]Mak B. A mathematical relationship between fullband and multiband mel-frequency cepstral coefficients[J]. IEEE Signal Processing Letters, 2002, 9(8): 241244.
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