Confidence Measures for Keyword Spotting using Suport Vector Machines

Abstract : Support Vector machines (SVM) is a new and very promising classification technique developed from the theory of Structural Risk Minimisation. In this paper, we propose an alternative out-of-vocabulary word detection method relying on confidence measures and support vector machines. Confidence measures are computed from phone level information provided by a Hidden Markov Model (HMM) based speech recognizer. We use three kinds of average techniques as arithmetic, geometric and harmonic averages to compute a confidence measure for each word. The acceptance/rejection decision of a word is based on the confidence feature vector which is processed by a SVM classifier. The performance of the proposed SVM classifier is compared with methods based on the averaging of confidence measures.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP'2003, Apr 2003, Hong Kong, Chine, 4 p, 2003
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https://hal.inria.fr/inria-00099706
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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-00099706, version 1

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Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet. Confidence Measures for Keyword Spotting using Suport Vector Machines. IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP'2003, Apr 2003, Hong Kong, Chine, 4 p, 2003. 〈inria-00099706〉

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