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Hidden markov model-based speech enhancement using multivariate laplace and gaussian distributions

Aroudi, A ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-spr.2014.0032
  3. Publisher: Institution of Engineering and Technology , 2015
  4. Abstract:
  5. 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-tonoise ratio levels which demonstrate the superiority of the proposed methods in most conditions in comparison with AR-HMM
  6. Keywords:
  7. Covariance matrix ; Discrete cosine transforms ; Discrete Fourier transforms ; Gaussian noise (electronic) ; Hidden Markov models ; Laplace transforms ; Markov processes ; Mean square error ; Speech enhancement ; Spurious signal noise ; Trellis codes ; Analytical studies ; Different distributions ; Experimental evaluation ; Gaussian assumption ; Multivariate Laplace ; Non-gaussian distribution ; Signaltonoise ratio (SNR) ; Speech enhancement algorithm ; Gaussian distribution
  8. Source: IET Signal Processing ; Volume 9, Issue 2 , 2015 , Pages 177-185 ; 17519675 (ISSN)
  9. URL: http://ieeexplore.ieee.org/document/7088717