Log-Domain Speech Feature Enhancement Using Sequential MAP Noise Estimation and a Phase-sensitive Model of the Acoustic Environment

In this paper we present an MMSE (minimum mean square error) speech feature enhancement algorithm, capitalizing on a new probabilistic, nonlinear environment model that effectively incorporates the phase relationship between the clean speech and the corrupting noise in acoustic distortion. The MMSE estimator based on this phase-sensitive model is derived and it achieves high efficiency by exploiting single-point Taylor series expansion to approximate the joint probability of clean and noisy speech as a multivariate Gaussian. As an integral component of the enhancement algorithm, we also present a new sequential MAP-based nonstationary noise estimator. Experimental results on the Aurora2 task demonstrate the importance of exploiting the phase relationship in the speech corruption process captured by the MMSE estimator. The phase-sensitive MMSE estimator reported in this paper performs significantly better than phase-insensitive spectral subtraction (54% error rate reduction), and also noticeably better than a phase-insensitive MMSE estimator as our previous state-of-the-art technique reported in [2] (7% error rate reduction), under otherwise identical experimental conditions of speech recognition.