Duration and Pronunciation Conditioned Lexical Modeling for Speaker Verification


Tur, G., Shriberg, E., Stolcke, A., & Kajarekar, S. (2007). Duration and pronunciation conditioned lexical modeling for speaker verification. In Eighth Annual Conference of the International Speech Communication Association.


We propose a method to improve speaker recognition lexical model performance using acoustic-prosodic information. More specifically, the lexical model is trained using duration- and pronunciation-conditioned word N-grams, simultaneously modeling lexical information along with their acoustic and prosodic characteristics. Support vector machines are used for modeling and scoring, with N-gram frequency vectors serving as features. Experimental results using NIST Speaker Recognition Evaluation data sets show that this method outperforms the regular word N-gram-based lexical models. Furthermore, our approach gives additional information when combined with a high-accuracy acoustic speaker model. We believe that this is a promising step toward integrated speaker recognition models that combine multiple types of high-level features.

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