Kundu, Achintya and Chatterjee, Saikat and Sreenivas, TV (2008) Subspace Based Speech Enhancement Using Gaussian Mixture Model. In: INTERSPEECH, Brisbane, Australia.Full text not available from this repository. (Request a copy)
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.Index Terms: Subspace based speech enhancement, Gaussian mixture density, MMSE estimation.
|Item Type:||Conference Paper|
|Department/Centre:||Division of Electrical Sciences > Electrical Communication Engineering|
|Date Deposited:||19 Sep 2011 08:53|
|Last Modified:||19 Sep 2011 08:53|
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