Language dependence in multilingual speaker verification

Abstract

An investigation into the performance of current speaker verification technology within a multilingual context is presented. Using the Oregon Graduate Institute (OGI) Multi-Language Telephone Speech Corpus (MLTS) database, the authors found that the performance of textindependent speaker verification depends fairly strongly on the language being spoken, with equal error rates differing by more than a factor of three between the best and worst performing languages. It was also found that training language-specific universal background models, to normalize speakers' scores, gives better results than both language-independent background models and background models derived from relevant language families.