Type-2 fuzzy GMM for text-independent speaker verification under unseen noise conditions

This paper describes a novel GMM-UBM based system that deals with the session noise variability problem. The system uses the Type-2 Fuzzy GMM framework by considering the speaker GMM parameters to be uncertain in an interval. The parameters intervals are estimated using a multicondition model training on noisy speeches that are synthesized from the speaker's utterances. Experiments were conducted using the MIT Device Speaker Verification Corpus with utterances having the lowest noise level as training data. The result shows an improvement in the EER of 24.11% for the proposed method compared to the GMM-UBM when evaluated over the noisiest utterances. This shows that the method reduces the effects of the session variability.