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Two-stage model-based feature compensation for robust speech recognition
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AI
DOI：10.1007/s00607-011-0152-1.png)
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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.
Keyword:
Batch-EM algorithm
Model-based feature compensation
Robust speech recognition
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C
IF:
2.8
论文数:
2.3K
被引数:
3.5K
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