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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.
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Submitted on : Tuesday, September 26, 2006 - 9:40:29 AM
Last modification on : Thursday, November 21, 2019 - 2:36:13 AM


  • HAL Id : inria-00099707, version 1


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. ⟨inria-00099707⟩



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