Abstract
Although multilayer perceptrons (MLPs) present several advantages against other pattern recognition methods, MLP-based speaker verification systems suffer from slow enrollment speed caused by many background speakers to achieve a low verification error. To solve this problem, the quantitative discriminative cohort speakers (QnDCS) method, by introducing the cohort speakers method into the systems, reduced the number of background speakers required to enroll speakers. Although the QnDCS achieved the goal to some extent, the improvement rate for the enrolling speed was still unsatisfactory. To improve the enrolling speed in this paper, the qualitative DCS (QlDCS) has been proposed by introducing a qualitative criterion to select less background speakers. An experiment for both methods is conducted to use the speaker verification system based on MLPs and continuants, and speech database. The results of the experiment show that the proposed QlDCS method enrolls speakers in shorter time than the QnDCS does.
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Lee, T.S., et al.: Faster Speaker Enrollment for Speaker Verification Systems Based on MLPs by Using Discriminative Cohort Speakers Method. LNCS (LNAI). Springer, Berlin (2003) (to be published)
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Lee, TS., Choi, SW., Choi, WH., Park, HT., Lim, SS., Hwang, BW. (2003). A Qualitative Discriminative Cohort Speakers Method to Reduce Learning Data for MLP-Based Speaker Verification Systems. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_155
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DOI: https://doi.org/10.1007/978-3-540-45080-1_155
Publisher Name: Springer, Berlin, Heidelberg
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