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 investigate the ability to recognize speaker using phonetic segments labels. We test the cooperation of several AR-Vector models, each one being learned on a distinct phonetic cluster. For this cooperation, we develop a segmental and an analytic approaches.
Bibliographic reference. Floch, J.-L. Le / Montacie, C. / Caraty, M.-J. (1995): "Speaker recognition experiments on the NTIMIT database", In EUROSPEECH-1995, 379-382.