NDLI: Speaker Recognition With Session Variability Normalization Based on MLLR Adaptation Transforms

Content Provider	IEEE Xplore Digital Library
Author	Stolcke, A. Kajarekar, S.S. Ferrer, L. Shrinberg, E.
Copyright Year	2006
Abstract	We present a new modeling approach for speaker recognition that uses the maximum-likelihood linear regression (MLLR) adaptation transforms employed by a speech recognition system as features for support vector machine (SVM) speaker models. This approach is attractive because, unlike standard frame-based cepstral speaker recognition models, it normalizes for the choice of spoken words in text-independent speaker verification without data fragmentation. We discuss the basics of the MLLR-SVM approach, and show how it can be enhanced by combining transforms relative to multiple reference models, with excellent results on recent English NIST evaluation sets. We then show how the approach can be applied even if no full word-level recognition system is available, which allows its use on non-English data even without matching speech recognizers. Finally, we examine how two recently proposed algorithms for intersession variability compensation perform in conjunction with MLLR-SVM.
Sponsorship	IEEE Signal Processing Society
Starting Page	1987
Ending Page	1998
Page Count	12
File Size	993143
File Format	PDF
ISSN	15587916
Volume Number	15
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speaker recognition Maximum likelihood linear regression Cepstral analysis Speech recognition Support vector machines Laboratories Linear regression NIST Feature extraction speaker recognition Intersession variability compensation maximum-likelihood linear regression–support vector machine (MLLR–SVM)
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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