Technical Program

Paper Detail

Paper:SP-P6.3
Session:Feature Analysis for ASR, TTS, and Verification
Time:Wednesday, May 19, 09:30 - 11:30
Presentation: Poster
Topic: Speech Processing: Feature Extraction
Title: VARIATIONAL BAYESIAN FEATURE SELECTION FOR GAUSSIAN MIXTURE MODELS
Authors: Fabio Valente; Institut Eurécom 
 Christian J. Wellekens; Institut Eurécom 
Abstract: 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.
 
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