GMM and ARVM cooperation and competition for text-independent speaker recognition on telephone speech

In order to improve the performances of speaker recognition on telephone speech, the authors investigate the ability to cooperate of two different modelling approaches: the GMM and the ARVM. For the cooperation and competition of the GMM and ARVM modelization, they used normalized measures. They develop two approaches for these cooperation and competition: a global approach and an analytical approach. They investigate experiments on whole sentences or selected phonetic segments. These approaches allow one to obtain performances improvements for both cooperation and competition, and good results on 168 speakers of the NTIMIT database (GMM: 61.7%, ARVM: 78.1%, cooperation: 79.9% and competition: 82.6%)