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Two-stage model-based feature compensation for robust speech recognition

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Abstract

This paper presents a combination approach to robust speech recognition by using two-stage model-based feature compensation. Gaussian mixture model (GMM)-based and hidden Markov model (HMM)-based compensation approaches are combined together and conducted sequentially in the multiple-decoding recognition system. The clean speech is firstly modeled as a GMM in the initial pass, and then modeled as a HMM generated from the initial pass in the following passes, respectively. The environment parameter estimation on these two modeling strategies are formulated both under maximum a posteriori (MAP) criterion. Experimental result shows that a significant improvement is achieved compared to European Telecommunications Standards Institute (ETSI) advanced compensation approach, GMM-based feature compensation approach, HMM-based feature compensation approach, and acoustic model compensation approach.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Haifeng Shen, Gang Liu & Jun Guo

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  1. Haifeng Shen
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  2. Gang Liu
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  3. Jun Guo
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Correspondence to Haifeng Shen.

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Shen, H., Liu, G. & Guo, J. Two-stage model-based feature compensation for robust speech recognition. Computing 94, 1–20 (2012). https://doi.org/10.1007/s00607-011-0152-1

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