MMSE SPEECH SPECTRAL AMPLITUDE ESTIMATION ASSUMING NON-GAUSSIAN NOISE

In many applications non-Gaussian noises, such as babble noise, can be observed. In this paper we present a minimum mean square error (MMSE) estimation of the speech spectral amplitude. It principally allows for arbitrary speech spectral amplitude probability density function (pdf) models (Rayleigh, Chi,...), while the pdf of the noise DFT coefficients is modeled by a Gaussian mixture (GMM). Applying for both approaches an idealized a priori SNR estimator that works well in babble noise, we can show clear improvements compared to the MMSE spectral amplitude estimator with Gaussian noise assumption.