Likelihood Score Normalization and Its Application in Text-Independent Speaker Verification

Deng Hao-jiang; Du Li-min; Wan Hong-jie

Volume 27 Issue 7

Jul. 2005

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2005 > 27(7): 1025-1029

Deng Hao-jiang, Du Li-min, Wan Hong-jie. Likelihood Score Normalization and Its Application in Text-Independent Speaker Verification[J]. Journal of Electronics & Information Technology, 2005, 27(7): 1025-1029.

Citation:

Deng Hao-jiang, Du Li-min, Wan Hong-jie. Likelihood Score Normalization and Its Application in Text-Independent Speaker Verification[J]. Journal of Electronics & Information Technology, 2005, 27(7): 1025-1029.

Citation:

PDF( 1020 KB)

Likelihood Score Normalization and Its Application in Text-Independent Speaker Verification

Received Date: 2004-02-23
Rev Recd Date: 2004-07-05
Publish Date: 2005-07-19

Abstract

Abstract

In this paper, the methodology of likelihood score normalization is studied. The text-independent speaker recognition system based on the adapted Gaussion Mixture Models(GMMs) is established, and the approach to normalize scores combining speaker-independent background model and the speaker-dependent models of cohort speaker sets are proposed. The speaker verification experiments over telephone channels show that based on the likelihood ratio of adapted GMMs system, both cohort normalization and hybrid score normalization approaches can improve the verification performance of baseline system using Universal Background Model (UBM). Specially, the hybrid approach combining UBM and cohort models selected during testing (T-cohort normalization) achieve the best performance. At a miss probability of 5%, the hybrid approach using UBM and T-cohort models reduce the false alarm rate to 0.5% compared to 2% for the baseline.

FullText(HTML)

References(1)

References

Doddington G R, Przybocki M A, Martin A F, Reynolds D A. The NIST speaker recognition - Overview, methodology, systems,results, perspective[J].Speech Communication.2000, 31(2- 3):225-[2]Reynolds D A. The effects of handset variability on speaker recognition performance: experiments on the switchboard corpus.In: Proc. ICASSP-1996, Atlanta, USA, May 1996:113 - 116.[3]Heck L P, Weintraub M. Handset dependent background models for robust text-independent speaker recognition. In: Proc.ICASSP-1992, Munich, Germany, 1997:1071 - 1074.[4]Reynolds D A, Quatieri T F, Dunn R B. Speaker verification using adapted Gaussian mixture models[J].Digital Signal Processing.2000, 10(1 - 3):19-[5]Dunn R B, Reynolds D A, Quatieri T F. Approaches to speaker detection and tracking in conversational speech[J].Digital Signal Processing.2000, 10(1 - 3):93-[6]Ariyaeeinia A M.[J].Sivakumaran P. Analysis and comparison of score normalization methods for text-dependent speaker verification. In Proc. EUROSPEECH97, Rhodes, Greece.1997,:-[7]Rosenberg A E, et al.. The use of cohort normalized scores for speaker verification. In Proc. ICSLP-1992, Banff, Canada, Nov.1992:599 - 602.[8]Colombi J, Ruck D, Rogers S, Oxley M, Anderson T. Cohort selection and word grammar effects for speaker recognition. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing,Atlanta, GA, 1996:85 - 88.[9]Higgins A, Bahler L, Porter J. Speaker verification using randomized phrase prompting[J].Digital Signal Processing.1991,1(2):89-[10]Reynolds D A. Comparison of background normalization methods for text-independent speaker verification. In Proc.EUROSPEECH97, Rhodes, Greece, 1997:963 - 966.[11]Reynolds D, Rose R. Robust text-independent speaker identification using Gaussian mixture speaker models[J].IEEE Trans. on Speech Audio Processing.1995, 3(1):72-[12]Martin A.[J].et al.. The DET curve in assessment of detection task performance. In Proc. EUROSPEECH97, Rhodos, Greece.1997,:-

Relative Articles

Supplements(0)

Cited By

Proportional views