A minimum mean square error approach for speech enhancement

A minimum mean square error (MMSE) estimation approach for enhancing speech signals degraded by statistically independent additive noise is developed, based upon Gaussian autoregressive (AR) hidden Markov modeling of the clean signal and Gaussian AR modeling of the noise process. The parameters of the models for the two processes are estimated from training sequences of clean speech and noise samples. It is shown that the MMSE estimator comprises a weighted sum of MMSE estimators for the individual output processes corresponding to the different states of the hidden Markov model for the clean speech. The weights at each time instant are the probabilities of the individual estimators to be the correct ones given the noisy speech. Typical signal-to-noise ratio (SNR) improvements achieved by this approach are 4.5-5.5 dB at 10-dB input SNR