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.