ICASSP 2007, 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing, 15-20 April 2007, Honolulu, USA
Many feature enhancement methods make use of probabilistic models of speech and noise in order to improve performance of speech recognizers in the presence of background noise. The traditional approach for training such models is maximum likelihood estimation. This paper investigates the novel application of variational Bayesian learning for front-end models under the Algonquin denoising framework. Compared to maximum likelihood training, it is shown that variational Bayesian learning has advantages both in terms of increased robustness with respect to choice of model complexity, as well as increased performan
Type:
Conference
City:
Honolulu
Date:
2007-04-15
Department:
Communication systems
Eurecom Ref:
2157
Copyright:
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