Automatic speech recognition with a modified Ephraim-Malah rule

A soft decision gain modification is introduced and applied to the Ephraim-Malah gain function based on maximum mean-square-error estimation after amplitude compression. Nonlinear evaluations of the noise overestimation factor and spectral floor are used in the same way for the proposed gain modification and for nonlinear spectral subtraction (NSS). Consistent and statistically significant automatic speech recognition improvements of the proposed approach with respect to NSS are observed for different noise conditions considered in the Aurora-2 and Aurora-3 corpora. As the nonlinearity affects the two approaches in the same way, the result of comparison is particularly interesting.