A Bayesian framework for robust speech enhancement under varying contexts

Abstract

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.

DOI: 10.1109/ICASSP.2012.6288932

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@article{Naidu2012ABF, title={A Bayesian framework for robust speech enhancement under varying contexts}, author={D. Hanumantha Rao Naidu and Sriram Srinivasan}, journal={2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2012}, pages={4557-4560} }