Speech Recognition under Multiple Noise Environment Based on Multi-Mixture HMM and Weight Optimization by the Aspect Model

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Abstract

In this paper, we propose an acoustic model that is robust to multiple noise environments, as well as a method for adapting the acoustic model to an environment to improve the model. The model is called “the multi-mixture model, ” which is based on a mixture of different HMMs each of which is trained using speech under different noise conditions. Speech recognition experiments showed that the proposed model performs better than the conventional multi-condition model. The method for adaptation is based on the aspect model, which is a “mixture-of-mixture” model. To realize adaptation using extremely small amount of adaptation data (i.e., a few seconds), we train a small number of mixture models, which can be interpreted as models for “clusters” of noise environments. Then, the models are mixed using weights, which are determined according to the adaptation data. The experimental results showed that the adaptation based on the aspect model improved the word accuracy in a heavy noise environment and showed no performance deterioration for all noise conditions, while the conventional methods either did not improve the performance or showed both improvement and degradation of recognition performance according to noise conditions.

Journal

  • IEICE Transactions on Information and Systems

    IEICE Transactions on Information and Systems 93(9), 2407-2416, 2010-09-01

    The Institute of Electronics, Information and Communication Engineers

References:  27

Cited by:  1

Codes

  • NII Article ID (NAID)
    10027640303
  • NII NACSIS-CAT ID (NCID)
    AA10826272
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    09168532
  • Data Source
    CJP  CJPref  J-STAGE 
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