On the application of hidden Markov models for enhancing noisy speech

An algorithm is proposed for enhancing noisy speech which has been degraded by statistically independent additive noise. The algorithm is based on modeling the clean speech as a hidden Markov process with mixtures of Gaussian autoregressive (AR) output processes and modeling the noise as a sequence of stationary, statistically independent, Gaussian AR vectors. The parameter sets of the models are estimated using training sequences from the clean speech and the noise process. The parameter set of the hidden Markov model is estimated by the segmental k-means algorithm. Given the estimated models, the enhancement of the noisy speech is done by alternate maximization of the likelihood function of the noisy speech, one over all sequences of states and mixture components assuming that the clean speech signal is given, and then over all vectors of the original speech using the resulting most probable sequence of states and mixture components. This alternating maximization is equivalent to first estimating the most probable sequence of AR models for the speech signal using the Viterbi algorithm, and then applying these AR models for constructing a sequence of Wiener filters which are used to enhance the noisy speech