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| 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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