Experiments in Session Variability Modelling for Speaker Verification

Presented is an approach to modelling session variability for GMM-based text-independent speaker verification incorporating a constrained session variability component in both the training and testing procedures. The proposed technique reduces the data labelling requirements and removes discrete categorisation needed by previous techniques and provides superior performance. Experiments on Mixer conversational telephony data show improvements of as much as 46% in equal error rate over a baseline system. In this paper the algorithm used for the enrollment procedure is described in detail. Results are also presented investigating the response of the technique to short test utterances and varying session subspace dimension

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