Title :
Nonlinear minimum mean square error estimator for mixture-maximisation approximation
Author :
Radfar, M.H. ; Banihashemi, A.H ; Dansereau, R.M. ; Sayadiyan, A.
Author_Institution :
Dept. of Syst. & Comput. Eng., Carleton Univ., Ottawa, Ont., Canada
fDate :
6/8/2006 12:00:00 AM
Abstract :
In many speech separation, enhancement, and recognition techniques, it is necessary to express the log spectrum of a mixture speech signal in terms of the log spectra of the underlying speech signals. For this purpose, the mixture-maximisation (MIXMAX) approximation is commonly used. Presented is a proof for this approximation in a statistical framework. It is concluded that this approximation is a nonlinear minimum mean square error estimator with the assumption of uniform distributions for phase information of the underlying speech signals.
Keywords :
least mean squares methods; optimisation; speech enhancement; speech recognition; statistical distributions; log spectrum; mean square error estimation; mixture-maximisation approximation; phase information; speech enhancement; speech recognition; speech separation; speech signals; statistical framework; uniform distributions;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20060510