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Using confidence measure for keyword detection in continuous speech recognition

By Yassine Benayed, Dominique Fohr, Jean-Paul Haton and Gérard Chollet


Colloque avec actes et comité de lecture. internationale.International audienceThis 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 distanc

Topics: 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, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Publisher: HAL CCSD
Year: 2004
OAI identifier: oai:HAL:inria-00100205v1
Provided by: HAL-UJM
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