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

Content Provider	IET Digital Library
Author	Aroudi, Ali Veisi, Hadi Sameti, Hossein
Abstract	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.
Starting Page	177
Ending Page	185
Page Count	9
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 2, Apr (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0032
Journal	IET Signal Processing
Publisher Date	2015-04-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A Priori Model AR Gaussian Distribution Hypothesis AR-HMM-based Enhancement Algorithm Autoregressive Gaussian Distribution Assumption Autoregressive Processes Covariance Matrices Diagonal Covariance Matrix Discrete Cosine Transform Discrete Cosine Transform Feature Vector Discrete Fourier Transform Gaussian Distribution Hidden Markov Model HMM-based Mean-square Error Estimator HMM-based MMSE Estimator Integral Transforms in Numerical Analysis Interpolation And Function Approximation Laplace Transforms Linear Algebra Markov Processes Mean Square Error Method Multivariate Laplace Distribution NonGaussian Distribution Numerical Analysis Online Adaptive Noise Suppression Algorithm Signal-to-noise Ratio Levels Speech Enhancement Speech Processing Technique Speech Recognition And Synthesis Speech Signal Statistical Speech Enhancement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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