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Parallel Speaker and Content Modelling for Text-Dependent Speaker Verification

Text-dependent short duration speaker verification involves two challenges. The primary challenge of interest is the verification of the speaker’s identity, and often a secondary challenge of interest is the verification of the lexical content of the pass-phrase. In this paper, we propose the use of two systems to handle these two tasks in parallel with one subsystem modelling speaker identity based on the assumption that lexical content is known and the other sub-system modelling lexical content in a speaker dependent manner. The text-dependent speaker verification sub-system is based on hidden Markov models and the lexical content verification system is based on models of speech segments that use a distinct Gaussian mixture model for each segment. Furthermore, a mixture selection method based on KL divergence was applied to refine the lexical content subsystem by making the models more discriminative. Experiments on part 1 of the RedDots database showed that the proposed combination of two sub-systems outperformed the baseline system by 39.8%, 51.1% and 37.3% in terms of the ‘imposter_correct’, ‘target_wrong’ and ‘imposter_wrong’ metrics respectively.