speech enhancement based on hidden markov model using sparse code shrinkage

This paper presents a new hidden markov model-based (hmm-based) speech enhancement framework based on the independent component analysis (ica). we propose analytical procedures for training clean speech and noise models using the baum re-estimation algorithm, and present a maximum a posteriori (map) estimator based on the laplace-gaussian (for clean speech and noise, respectively) combination in the hmm framework, namely sparse code shrinkage-hmm (scs-hmm). the proposed method on the timit database in the presence of three noise types at three snr levels in terms of pesq and snr are evaluated and compared with auto-regressive hmm (ar-hmm) and speech enhancement based on hmm with discrete cosine transform (dct) coefficients using the laplace and gaussian distributions (laga-hmmdct). the results obtained confirm the superiority of the scs-hmm method in the presence of non-stationary noises compared to laga-hmmdct. the results of the scs-hmm method represent a better performance of this method compared to ar-hmm in the presence of white noise based on the pesq measure