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Using confidence measure for keyword detection in continuous speech recognition

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
2 LHC - Laboratoire Hubert Curien [Saint Etienne]
Abstract : This paper deals with the problem of detection keywords/rejection out-of-vocabulary in continuous speech recognition. Two different techniques based on confidence measures are investigated to improve the detection of keywords using a grammar founded on loop of phones. Confidence measures are computed from phone level information provided by a Hidden Markov model based speech recognizer. We use two kinds of likelihood as ratio and distance, and theirs normalised forms to compute a confidence measures for each word. All confidence measures are are evaluated using the French SPEECHDAT database. The Figure-Of-Merite (FOM) for the normalized likelihood ratio is about $68.2\%$ compared to $71.5\%$ obtained by the normalized likelihood distance
Mots-clés : hidden markov models. automatic speech recognition keyword detection confidence measure détection de mots clés mesure de confiance modèle de markov caché reconnaissance de la parole
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Conference papers
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https://hal.inria.fr/inria-00100205
Contributor : Publications Loria Connect in order to contact the contributor
Submitted on : Tuesday, September 26, 2006 - 10:15:29 AM
Last modification on : Thursday, January 20, 2022 - 5:26:59 PM

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  • HAL Id : inria-00100205, version 1

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INRIA | UNIV-ST-ETIENNE | LAHC | CNRS | INRIA2 | LORIA | UNIV-LORRAINE | UDL | IOGS | PARISTECH

Citation

Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet. Using confidence measure for keyword detection in continuous speech recognition. Conférence Internationale sur l'accès Intelligent aux Documents Multimédia sur l'Internet - Medinet'04, 2004, Tozeur, Tunisie, 10 p. ⟨inria-00100205⟩

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