Sparse Hidden Markov Models for Speech Enhancement in Non-Stationary Noise Environments

We propose a sparse hidden Markov model (HMM)-based single- channel speech enhancement method that models the speech and noise gains accurately in non- stationary noise environments. Autoregressive models are employed to describe the speech and noise in a unified framework and the speech and noise gains are modeled as random processes with memory. The likelihood criterion for finding the model parameters is augmented with an l(p) regularization term resulting in a sparse autoregressive HMM (SARHMM) system that encourages sparsity in the speech- and noise- modeling. In the SARHMM only a small number of HMM states contribute significantly to the model of each particular observed speech segment. As it eliminates ambiguity between noise and speech spectra, the sparsity of speech and noise modeling helps to improve the tracking of the changes of both spectral shapes and power levels of non-stationary noise. Using the modeled speech and noise SARHMMs, we first construct a noise estimator to estimate the noise power spectrum. Then, a Bayesian speech estimator is derived to obtain the enhanced speech signal. The subjective and objective test results indicate that the proposed speech enhancement scheme can achieve a larger segmental SNR improvement, a lower log- spectral distortion and a better speech quality in stationary noise conditions than state-of-the-art reference methods. The advantage of the new method is largest for non-stationary noise conditions.