Compensation of Nuisance Factors for Speaker and Language Recognition

The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian mixture models, while in the feat...

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Bibliographic Details
Published in:IEEE transactions on audio, speech, and language processing speech, and language processing, 2007-09, Vol.15 (7), p.1969-1978
Main Authors: Castaldo, F.., Colibro, D.., Dalmasso, E.., Laface, P.., Vair, C..
Format: Article
Language:English
Subjects:
Acoustic distortion
Acoustic testing
Automatic speech recognition
Channels
Compensation
Computational efficiency
Estimating
Factor analysis
feature compensation
Gaussian
Gaussian distribution
language recognition
Loudspeakers
Mathematical models
Natural languages
NIST
Nuisance
Recognition
Speaker recognition
Speech recognition
Studies
System testing
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Summary:The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian mixture models, while in the feature domain blind channel compensation is usually performed. The aim of this work is to explore techniques that allow more accurate intersession compensation in the feature domain. Compensating the features rather than the models has the advantage that the transformed parameters can be used with models of a different nature and complexity and for different tasks. In this paper, we evaluate the effects of the compensation of the intersession variability obtained by means of the channel factors approach. In particular, we compare channel variability modeling in the usual Gaussian mixture model domain, and our proposed feature domain compensation technique. We show that the two approaches lead to similar results on the NIST 2005 Speaker Recognition Evaluation data with a reduced computation cost. We also report the results of a system, based on the intersession compensation technique in the feature space that was among the best participants in the NIST 2006 Speaker Recognition Evaluation. Moreover, we show how we obtained significant performance improvement in language recognition by estimating and compensating, in the feature domain, the distortions due to interspeaker variability within the same language.
ISSN:1558-7916
2329-9290
1558-7924
2329-9304
DOI:10.1109/TASL.2007.901823