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)
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)
The delta coefficients are a conventional method to include temporal information in the speech recognition systems. In particular, they are widely used in the gaussian HMM-based systems. Some attempts were made to introduce the delta coefficients in the K-Nearest Neighbours (K-NN) HMM-based system that we recently developed. An introduction of the delta(More)