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Conference papers

Confidence Measures for Keyword Spotting using Suport Vector Machines

Yassine Benayed 1 Dominique Fohr 1 Jean-Paul Haton 1 Gérard Chollet 2
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
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Submitted on : Tuesday, September 26, 2006 - 9:40:29 AM
Last modification on : Thursday, January 20, 2022 - 5:26:59 PM


  • HAL Id : inria-00099706, version 1


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. ⟨inria-00099706⟩



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