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                <term xml:lang="fr">automatic speech recognition</term>
                <term xml:lang="fr">keyword detection</term>
                <term xml:lang="fr">confidence measure</term>
                <term xml:lang="fr">détection de mots clés mesure de confiance</term>
                <term xml:lang="fr">modèle de markov caché</term>
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                <term xml:lang="fr">reconnaissance de la parole</term>
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              <p>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</p>
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