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

Yuuji MUKAI, Hideki NODA, Takashi OSANAI

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E94-D No.1 pp.162-166
Publication Date
2011/01/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E94.D.162
Type of Manuscript
LETTER
Category
Speech and Hearing

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