Session variability in Automatic Speaker Verification

This paper explores the use of mismatch condition in speaker variability applied to Automatic Speaker Verification (ASV) defined as a classification task to decide whether a proclaimed identity is true or not. This paper proposes to model mismatch conditions in speaker variability from session to another. It was shown that the speaker recognition accuracy deteriorates when there is an acoustic mismatch between the speech obtained during training and testing. The target speakers are modeled using vector quantization (VQ) approach based on multiples session codebooks, and then compared it to baseline vector quantization. The present study demonstrates that the session variability for target speaker improve the performance of ASV system and the several codebooks for target speaker (set of session codebooks) give better results.. The performance of these models is evaluated on the Arabic speaker verification dataset.