Marie-José Caraty

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In this paper, we study various technics to improve the performance, to reduce the computation cost and the required memory of a recognition system based on HMM. For the efficiency of the system, we first study the optimization of the number of HMM parameters according to training data. We experiment a temporal control of the phonetic transitions on lexical(More)
In this paper, we compare three speaker recognition systems results (i.e. GMM, AHSM, ARVM) on the TIMIT and NTIMIT databases. In order to improve the results on the NTIMIT database, we present two more sophisticated systems: the first one is based on ARMA-Vector model, the second one is based on the utilisation of several AR-Vector models per speaker. We(More)
Most speaker-independent acoustic-phonetic decoding systems are based on hidden Markov models. Such systems lack a real temporal control for the phonetic models. Furthermore, inter-speaker variability makes speaker adaptation necessary. In order to solve these problems, we introduce two original approaches. On the one hand, discontinuities detected with the(More)
In order to improve the performances of speaker recognition on telephone speech, we investigate the ability to cooperate of two different natures modelizations: the GMM and the ARVM. For the cooperation and competition of the GMM and ARVM modelizations, we used normalized measures. We develop two approaches for these cooperation and competition : a global(More)