Variational Bayesian feature selection for gaussian mixture models

Valente, Fabio;Wellekens, Christian J
ICASSP 2004, 29th IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, 2004, Montreal, Canada

In this paper we show that feature selection problem can be formulated as a model selection problem. A Bayesian framework for feature selection in unsupervised learning based on Gaussian Mixture Models is applied to speech recognition. In the original formulation (see [1]) a Minimum Message Length criterion is used for model selection; we propose a new model selection technique based on Variational Bayesian Learning that shows a higher robustness to amount of training data. Results on speech data from the TIMIT database show a high efficiency in determining feature saliency.


DOI
Type:
Conférence
City:
Montreal
Date:
2004-05-17
Department:
Sécurité numérique
Eurecom Ref:
1321
Copyright:
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