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

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... ase 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.