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- W146072112 abstract "We propose a novel design for acoustic feature-based automatic spoken language recognizers. Our design is inspired by recent advances in text-independent speaker recognition, where intraclass variability is modeled by factor analysis in Gaussian mixture model (GMM) space. We use approximations to GMMlikelihoods which allow variable-length data sequences to be represented as statistics of fixed size. Our experiments on NIST LRE’07 show that variability-compensation of these statistics can reduce error-rates by a factor of three. Finally, we show that further improvements are possible with discriminative logistic regression training. Index Terms: acoustic language recognition, intersession variability compensation, discriminative training" @default.
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- W146072112 date "2009-09-06" @default.
- W146072112 modified "2023-09-26" @default.
- W146072112 title "Discriminative acoustic language recognition via channel-compensated GMM statistics" @default.
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- W146072112 doi "https://doi.org/10.21437/interspeech.2009-623" @default.
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