Subspace based speech enhancement using Gaussian mixture model
2008 (English)In: INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 2008, 395-398 p.Conference paper (Refereed)
Traditional subspace based speech enhancement (SSE) methods use linear minimum mean square error (LMMSE) estimation that is optimal if the Karhunen Loeve transform (KLT) coefficients of speech and noise are Gaussian distributed. In this paper, we investigate the use of Gaussian mixture (GM) density for modeling the non-Gaussian statistics of the clean speech KLT coefficients. Using Gaussian mixture model (GMM), the optimum minimum mean square error (MMSE) estimator is found to be nonlinear and the traditional LMMSE estimator is shown to be a special case. Experimental results show that the proposed method provides better enhancement performance than the traditional subspace based methods.
Place, publisher, year, edition, pages
2008. 395-398 p.
Subspace based speech enhancement, Gaussian mixture density, MMSE estimation
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-46571ISI: 000277026100105ISBN: 978-1-61567-378-0OAI: oai:DiVA.org:kth-46571DiVA: diva2:453894
INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association; Brisbane, QLD; 22 September 2008 through 26 September 2008
QC 201111072011-11-032011-11-032012-01-13Bibliographically approved