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- W2392862364 abstract "The distant-talking speech recognition would be affected by reverb in a enclosed environment. As a result,the recognition rate would be greatly reduced. Reverberation modeling for speech recognition( REMOS) is a new method for reverberate compensation in the model domain; it can improve distant-talking speech recognition accuracy effectively if the sound source location is already known. But in a real application,location of sound source can be hardly to predicted. Based on the principle of maximum a posteriori probability and frame-wise hidden Markov model( HMM) model compensation,a new method for model compensation in a enclosed environment is proposed in this paper. In this method,K-means clustering algorithm is used to cluster room impulse response( RIR) optimized sets,and merge the reverberation model which is in a same kind class,then Viterbi decoding algorithm is loaded,and frame-wise compensation is implemented to the clear speech HMM model. At last,the best compensate model is selected through the maximum a posteriori estimation. It makes model domain reverberate compensation to be closest to the accurate compensation. The experimental results prove that the method can enhance distant-talking speech recognition accuracy further." @default.
- W2392862364 created "2016-06-24" @default.
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- W2392862364 date "2014-01-01" @default.
- W2392862364 modified "2023-09-28" @default.
- W2392862364 title "REMOS-based method for model domain compensation in remote speech recognition" @default.
- W2392862364 hasPublicationYear "2014" @default.
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