Speech enhancement based on hidden Markov model using sparse code shrinkage

• Main Authors: E. Golrasan, H. Sameti
• Format: Article
• Language: English
• Published: Shahrood University of Technology 2016-07-01
• Series: Journal of Artificial Intelligence and Data Mining
• Subjects: Speech Signal Enhancement; HMM-based Speech Enhancement; Multivariate Laplace Distribution; Independent Component Analysis (ICA transform); Sparse Code Shrinkage Enhancement Method
• Online Access: http://jad.shahroodut.ac.ir/article_653_0c94d7b0e43ae42476963def7205ed95.pdf
• ISSN: 2322-5211; 2322-4444

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.