Open-set speaker identification in broadcast news

Abstract

In this paper, we examine the problem of text-independent open-set speaker identification (OS-SI) in broadcast news. Particularly, the impact of the population of registered speakers to OS-SI performance is investigated, which is the central issue for designing practical OS-SI system. We amend the maximum mutual information (MMI)-based discriminative training scheme to facilitate its incorporation in OS-SI systems. We also improve the implementation to allow the application of MMI based approach with 2048-component Gaussian mixture models. All systems are evaluated using NIST RT-03, RT-04 and FBIS corpora, with a maximum of 82 registered speakers. Our study shows that notable performance improvement can be obtained with MMI-based discriminative training, which reduces the equal error rate (EER) by 15.9% relatively, in comparison to the GMM-MAP scheme.