A New Keyword Spotting Approach Based on Reward Function

Abstract : In this paper, we compare the performance achieved by different word-spotting techniques based on hidden Markov models. We propose two methods to detect keywords, the first one uses a GMM (Gaussian Mixture Model) as a filler model to absorb the out-of-vocabulary words. The second is an alternative approach which does not attempt to model out-of-vocabulary words, instead, it uses buckled phonemes basedgrammar. Furthermore, it uses different reward functions to favourite the recognition of the keywords phonemes.
Type de document :
Communication dans un congrès
Eventh International Symposium on Signal Processing and Its Applications - ISSPA'2003, Jul 2003, Paris, France, 4 p, 2003
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https://hal.inria.fr/inria-00099707
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 09:40:29
Dernière modification le : jeudi 11 janvier 2018 - 06:19:57

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  • HAL Id : inria-00099707, version 1

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Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet. A New Keyword Spotting Approach Based on Reward Function. Eventh International Symposium on Signal Processing and Its Applications - ISSPA'2003, Jul 2003, Paris, France, 4 p, 2003. 〈inria-00099707〉

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