Sparse HMM-based speech enhancement method for stationary and 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 both stationary and nonstationary environments. The objective function is augmented with an lp regularization term resulting in a sparse autoregressive HMM (SARHMM). The method encourages sparsity in the speech- and noise- modeling, which eliminates the ambiguity between noise and speech spectra and, as a consequence, provides improved 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 an estimator to estimate the noise spectrum. Then a Bayesian speech estimator is used to obtain the enhanced speech. The test results indicate that the proposed speech enhancement scheme performs much better than the reference methods in non-stationary environments, while providing state-of-the-art performance for stationary conditions.