Discriminative acoustic language recognition via channel-compensated GMM statistics
| Autor: | Lukas Burget, Valiantsina Hubeika, Albert Strasheim, Niko Brümmer, Ondrej Glembek, Pavel Matejka |
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| Rok vydání: | 2009 |
| Předmět: |
Channel (digital image)
Computer science business.industry Speech recognition Pattern recognition Mixture model Speaker recognition Discriminative model Factor (programming language) Statistics Feature (machine learning) NIST Artificial intelligence business computer computer.programming_language Spoken language |
| Zdroj: | INTERSPEECH Scopus-Elsevier |
| DOI: | 10.21437/interspeech.2009-623 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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