Text-Independent Speaker Verification Using Artificially Generated GMMs for Cohorts
IEICE - Transactions on Information and Systems
Rejection of non-meaningful activities for HMM-based activity recognition system
Image and Vision Computing
Proceedings of the 2nd Conference on Wireless Health
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Most state-of-the-art speaker verification systems need a user model built from samples of the customer speech, and a speaker independent (SI) background model with high acoustic resolution. These systems rely heavily on the availability of speaker independent databases along with a priori knowledge about acoustic rules of the utterance, and depend on the consistency of acoustic conditions under which the SI models were trained. These constraints may be a burden in practical and portable devices such as palm-top computers or wireless handsets which place a premium on computation and memory, and where the user is free to choose any password utterance in any language, under any acoustic condition. In this paper, we present a novel and reliable approach to background model design when only the enrollment data is available. Preliminary results are provided to demonstrate the effectiveness of such systems.