In this article, we present a novel mechanism by which more precise voiceprints can be constructed in a typical text-dependent speaker verification system based on a continuous density hidden Markov model (HMM). Typical voiceprints (speaker-dependent HMMs) are first trained using a subscriber's enrollment data. The resulting models are then restructured to permit a modeling of sub-state behavior. At first, the restructured models are functionally equivalent to the conventional voiceprint. Sub-state parameters are then estimated by the re-application of the enrollment data. The resulting speaker-dependent models provide improved speaker verification performance relative to the models with the original topology.
Cite as: Peters, S.D., Hébert, M., Boies, D. (2000) Transition-oriented hidden Markov models for speaker verification. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 2, 270-273
@inproceedings{peters00_icslp,
author={S. Douglas Peters and Matthieu Hébert and Daniel Boies},
title={{Transition-oriented hidden Markov models for speaker verification}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 2, 270-273}
}