Compensation of Nuisance Factors for Speaker and Language Recognition

• 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
• ISSN: 1558-7916; 2329-9290; 1558-7924; 2329-9304
• DOI: 10.1109/TASL.2007.901823

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.