Markov model based noise modeling and its application to noisy speech recognition using dynamical features of speech

In this paper, some algorithms to recognize speech in time varying noise are proposed. In the proposed methods, spectral subtraction and Markov model based noise models are successfully utilized in the framework of spectral decomposition of noisy speech. Firstly, we considered the problem of the mis-subtraction noise which is caused in the subtraction based decomposition procedure. Then, the precise use of dynamical feature of speech such as delta cepstrum is discussed. Using the methods proposed here, recognition performance are improved more than 60% compared to no compensation method.<<ETX>>

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