Mixed environment compensation based on maximum a posteriori estimation for robust speech recognition

Authors:
Haifeng Shen;Gang Liu;Jun Guo
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China 100876;Beijing University of Posts and Telecommunications, Beijing, China 100876;Beijing University of Posts and Telecommunications, Beijing, China 100876
Venue:
Artificial Intelligence Review
Year:
2009

Citing 6
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Abstract

Noise robustness is a fundamental problem for speech recognition system in the real environments. The paper presents mixed environment compensation technique in which feature compensation algorithm and acoustic model compensation algorithm is combined together. The target is to obtain the fine compensated static acoustic model and the dynamic compensated speech. Therefore, the modified speech sequence can well match the modified acoustic model. The experimental results show that significant performance improvement has been observed.