9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Subspace Based Speech Enhancement Using Gaussian Mixture Model

Achintya Kundu, Saikat Chatterjee, T. V. Sreenivas

Indian Institute of Science, India

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.

Full Paper

Bibliographic reference.  Kundu, Achintya / Chatterjee, Saikat / Sreenivas, T. V. (2008): "Subspace based speech enhancement using Gaussian mixture model", In INTERSPEECH-2008, 395-398.