Sparse HMM-based speech enhancement method for stationary and non-stationary noise environments

Feng, De‐Cheng; Bao, Changchun; Kleijn, W. Bastiaan

doi:10.1109/icassp.2015.7178937

Cited by 6 publications

(1 citation statement)

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“…ARHMM is further improved and the speech and noise gains are considered as random processes that describe the power levels of speech and noise [9]. By learning the speech and noise characteristics on-line, prior information of the gains can be obtained the more accurately.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity

Gayathri¹,

Chenchamma²,

Kumar³

2017

IJCA

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Speech enhancement is required to enhance the quality of speech corrupted by the background noise and can be used in many applications such as hearing aids, mobile communication etc. In this paper a speech enhancement method is presented in which first Autoregressive (AR) model is applied for the noisy speech signal to find the speech parameters and then Hidden Markov model is applied to model those parameters. Later, the sparsity is encouraged into the model by adding the regularization parameter. The objective results for the proposed method and Wiener filter are compared. Speech quality in non-stationary noise conditions is observed through listening. The average loglikelihood score is obtained for different noises and observed that the performance is improved compared to the reference methods. KeywordsSpeech enhancement, non-stationary noise, sparse autoregressive hidden markov model (SARHMM).

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Section: Introductionmentioning

confidence: 99%