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
Variational Bayesian learning of speech GMMS for feature enhancement based on Algonquin
ICASSP 2007, 32nd IEEE International Conference on Acoustics, Speech, and Signal Processing, 15-20 April 2007, Honolulu, USA
Systèmes de Communication
© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PERMALINK : https://www.eurecom.fr/publication/2157