Speech enhancement based on hidden Markov model using sparse code shrinkage
Author(s):
Abstract:
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 by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM framework, namely sparse code shrinkage-HMM (SCS-HMM).
The proposed method on 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 Laplace and Gaussian distributions (LaGa-HMMDCT). The results confirm the superiority of SCS-HMM method in presence of non-stationary noises compared to LaGa-HMMDCT. The results of SCS-HMM method represent better performance of this method compared to AR-HMM in presence of white noise based on PESQ measure.
The proposed method on 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 Laplace and Gaussian distributions (LaGa-HMMDCT). The results confirm the superiority of SCS-HMM method in presence of non-stationary noises compared to LaGa-HMMDCT. The results of SCS-HMM method represent better performance of this method compared to AR-HMM in presence of white noise based on PESQ measure.
Keywords:
Language:
English
Published:
Journal of Artificial Intelligence and Data Mining, Volume:4 Issue: 2, Summer-Autumn 2016
Pages:
213 to 218
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