Discriminative Score Fusion for LanguageIdenti¯cation
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Graphical Abstract
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Abstract
Language identi¯cation (LID) has received
increasing interests in the speech signal processing com-
munity. With the rapid development of LID technologies,
how to fuse the score of multi-systems is growing to be a
researching focus. In this paper, we proposed a discrimina-
tive framework for LID score fusion. The Heteroscedastic
linear discriminate analysis (HLDA) technology is used for
dimension reduction and de-correlation, and the Gaussian
mixture model (GMM) trained with Maximum mutual in-
formation (MMI) criteria is used as classi¯er. Experiments
show that the proposed method can improve the perfor-
mance signi¯cantly. By score fusion of ¯ve systems, we
achieve average cost of 2.10% for 30s trials on the 2007
NIST language recognition evaluation databases.
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