Speech enhancement using a Bayesian evidence approach
Abstract We consider the enhancement of speech corrupted by additive white Gaussian noise. In a Bayesian inference framework, maximum a posteriori (MAP) estimation of the signal is performed, along the lines developed by Lim & Oppenheim (1978). The speech enhancement problem is treated as a signal estimation problem, whose aim is to obtain a MAP estimate of the clean speech signal, given the noisy observations. The novelty of our approach, over previously reported work, is that we relate the variance of the additive noise and the gain of the autoregressive (AR) process to hyperparameters in a hierarchical Bayesian framework. These hyperparameters are computed from the noisy speech data to maximize the denominator in Bayes formula, also known as the evidence. The resulting Bayesian scheme is capable of performing speech enhancement from the noisy data without the need for silence detection. Experimental results are presented for stationary and slowly varying additive white Gaussian noise. The Bayesian scheme is also compared to the Lim and Oppenheim system, and the spectral subtraction method.