Speaker recognition experiments on the NTIMIT database
Jean-Luc Le Floch, Claude Montacié, M.-J. Caraty · 1995
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 ARMAVector 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. 1. INTRODUCTION Speaker recognition refers to two problems which are the verification of the claimed identity and the speaker identification. In this paper, we are especially interested in speaker identification in phone quality speech. This task is of a high interest in vocal applications through the telephone network. We use for our experiments the NTIMIT database [1]. The NTIMIT...