Artificial Cohort Generation Based on Statistics of Real Cohorts for GMM-Based Speaker Verification

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概要

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• This paper discusses speaker verification (SV) using Gaussian mixture models (GMMs), where only utterances of enrolled speakers are required. Such an SV system can be realized using artificially generated cohorts instead of real cohorts from speaker databases. This paper presents a rational approach to set GMM parameters for artificial cohorts based on statistics of GMM parameters for real cohorts. Equal error rates for the proposed method are about 10% less than those for the previous method, where GMM parameters for artificial cohorts were set in an ad hoc manner.

• 2011-01-01

著者

• MUKAI Yuuji

  Department of Systems Design and Informatics, Kyushu Institute of Technology

• NODA Hideki

  Department of Systems Design and Informatics, Kyushu Institute of Technology

• OSANAI Takashi

  National Research Institute of Police Science

• Noda Hideki

  Kyushu Inst. Technol. Iizuka Jpn

• Noda Hideki

  Kyushu Institute Of Technology Dept. Of Systems Innovation And Informatics

• Mukai Yuuji

  Department Of Systems Design And Informatics Kyushu Institute Of Technology

• Noda Hideki

  Department Of Applied Biochemistry Faculty Of Applied Biological Science Hiroshima University

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