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Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity

by A. Gayathri, G. Chenchamma, K. V. V. Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 2
Year of Publication: 2017
Authors: A. Gayathri, G. Chenchamma, K. V. V. Kumar
10.5120/ijca2017912640

A. Gayathri, G. Chenchamma, K. V. V. Kumar . Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity. International Journal of Computer Applications. 157, 2 ( Jan 2017), 29-39. DOI=10.5120/ijca2017912640

@article{ 10.5120/ijca2017912640,
author = { A. Gayathri, G. Chenchamma, K. V. V. Kumar },
title = { Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 2 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number2/26807-2017912640/ },
doi = { 10.5120/ijca2017912640 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:02:54.197668+05:30
%A A. Gayathri
%A G. Chenchamma
%A K. V. V. Kumar
%T Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 2
%P 29-39
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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 log-likelihood score is obtained for different noises and observed that the performance is improved compared to the reference methods.

References
  1. Lawrence R. Rabiner and Ronald W. Schafer, Digital Processing of Speech Signals. Prentice-Hall, Inc., Englewood Cliffs, New Jersey 07632.
  2. Lawrence R. Rabiner and Ronald W. Schafer, Introduction to Digital Speech Processing.
  3. Thomas F. Quatieri, Discrete-Time Speech Processing, Principles and Practice.
  4. Lawrence R. Rabiner and Biing-Hwang Juang, Fundamentals of Speech Recognition, Prentice-Hall, Signal Processing Series.
  5. L. Rabiner, “A tutorial on Hidden Markov models and Selected Applications in Speech Recognition,” proc. IEEE, vol. 77, no. 2, Feb. 1989.
  6. Philipos C. Loizou, Speech Enhancement: Theory and practice, second edition, CRC press.
  7. Simon Haykin, Adaptive Filter Theory, third edition, Prentice-Hall, Information and System Sciences Series.
  8. Feng Deng, Changchun Bao, and W. Bastiaan Kleijn, Sparse Hidden Markov Models for speech Enhancement in Non-Stationary Noise Environments, IEEE Trans. Audio, Speech, Lang. Process., Vol. 23, no. 11, Nov. 2015.
  9. Feng Deng, Changchun Bao, and W. Bastiaan Kleijn, “Sparse HMM-based Speech Enhancement method for Stationary and Non-Stationary Noise Environments,” in proc. IEEE International conf. on Acoustics, Speech and signal Processing (ICASSP), 2015.
  10. D. Y. Zhao and W. B. Kleijn, “HMM-based gain modeling for enhancement of speech in noise,” IEEE Trans. Audio, Speech, Lang. Process., Vol. 15, no. 3, Mar. 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Speech enhancement non-stationary noise sparse autoregressive hidden markov model (SARHMM).

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