GMM-UBM Modeling for Speaker Recognition on a Romanian Large Speech Corpora

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Abstract

In recent years, non-intrusive biometric identification systems have evolved very quickly. Their main advantage over classical systems, based on usernames and passwords, is the fact that the anatomical features are less exposed to stealing or losing. In this context, this study deals with speaker recognition - one of the most fashionable biometric technologies. It presents speaker verification and speaker identification experiments carried out on Romanian speech, using the GMM-UBM framework in different configurations. The experiments are performed on a corpus about 5 times larger than other previous attempts for Romanian language. It comprises connected digits uttered in Romanian by over 120 speakers. The results show that the false rejection rate is close to 0% and false acceptance rate is around 1.50%. For speaker identification, the results are relatively good for the closed-set scenario (identification error < 1%), but not still acceptable for the open-set scenario. The error rates were computed in different configurations, error trends being obtained depending on the variation of some main system parameters.

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