Skip Navigation

IEICE Transactions on Information and Systems 2006 E89-D(3):1040-1049; doi:10.1093/ietisy/e89-d.3.1040
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by DAT, T. H.
Right arrow Articles by ITAKURA, F.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © 2006 The Institute of Electronics, Information and Communication Engineers

Special Section on Statistical Modeling for Speech Processing -- Papers -- Speech Enhancement

Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement

Tran Huy DAT1,3, Kazuya TAKEDA1 and Fumitada ITAKURA2

1 The authors are with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464–8603 Japan. E-mail: hdtran{at}i2r.a-star.edu.sg, 2 The author is with the Graduate School of Information Engineering, Meijo University, Nagoya-shi, 468–8502 Japan., 3 Presently, with the Institute for Infocomm Research, Singapore.

This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.

Key Words: speech enhancement, speech recognition, gamma modeling, fourth-order moment, MMSE, MAP, spectral magnitude, power, log-spectral magnitude

Manuscript received June 11, 2005. Manuscript revised September 30, 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.