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    Abstract:

    In this paper, a novel adaptive text-independent speaker verification method is proposed. This adaptive method is based on the previous work, which uses global speaker model to normalize the likelihood score, and solves one problem of the previous method, i.e., the training time is to long. As a consequence, the waiting time for a new registration is shortened so that a new user can use the system in a short period. The experimental results fully demonstrate the effectiveness of this novel method. When the system has 30 users, the registration time for a new user is accelerated 12 times.

    Reference
    1  Soong A E, Rosenberg L R, Rabiner et al. A vector quantization approach to speaker recognition. AT&T Technology Journal, 1987,66(2):14~26 2  Tishby N. On the application of mixture AR hidden Markov models to text independent speaker recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 1991,39(3):563~570 3  Zhang Yi-ying, Zhu Xiao-yan, Zhang Bo. A new speaker verification method with global speaker model and likelihood score normalization. Journal of Computer Science and Technology, 2000,15(2):184~193 4  Liporace L A. Maximum likelihood estimation for multivariate observations of Markov sources. IEEE Transactions on Information Theory, 1982,IT-28(5):729~734 5  Rabiner L R, Juang S E, Sondhi M M. Recognition of isolated digits using hidden Markov models with continuous mixture densities. AT&T Technology Journal, 1986,64(6):1211~1222
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张怡颖,朱小燕,张钹.与文本无关的说话人自适应确认方法.软件学报,2000,11(6):799-803

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History
  • Received:February 04,1999
  • Revised:June 09,1999
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