A Bayesian framework for robust speech enhancement under varying contexts

Single-microphone speech enhancement algorithms that employ trained codebooks of parametric representations of speech spectra have been shown to be successful in the suppression of non-stationary noise, e.g., in mobile phones. In this paper, we introduce the concept of a context-dependent codebook, and look at two aspects of context: dependency on the particular speaker using the mobile device, and on the acoustic condition during usage (e.g., hands-free mode in a reverberant room). Such context-dependent codebooks may be trained on-line. A new scheme is proposed to appropriately combine the estimates resulting from the context-dependent and context-independent codebooks under a Bayesian framework. Experimental results establish that the proposed approach performs better than the context-independent codebook in the case of a context match and better than the context-dependent codebook in the case of a context mismatch.