| 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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