Hidden Markov model-based speech enhancement using multivariate Laplace and Gaussian distributions

In this paper, statistical speech enhancement using hidden Markov model (HMM) is studied and new techniques for applying non-Gaussian distributions are proposed. The superiority of using non-Gaussian distributions in online adaptive noise suppression algorithms has been proven; however, in this study, this approach is formulated in an HMM-based mean-square error estimator (MMSE) estimator in which a priori models are trained in an off-line manner. In addition, an analytical study of using different distributions other than autoregressive (AR) Gaussian distribution, such as Laplace, is presented in order to construct an accurate HMM as a priori model for discrete Fourier transform and discrete cosine transform feature vectors of speech signal. In the proposed framework, an HMM-based MMSE estimator bassed on Gaussian assumption using diagonal covariance matrix is provided rather than AR hypothesis which is employed in the conventional AR-HMM-based speech enhancement algorithm. Experimental evaluations of the proposed methods are done in the presence of four different noise types at various signal-to-noise ratio levels which demonstrate the superiority of the proposed methods in most conditions in comparison with AR-HMM.