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http://dx.doi.org/10.7776/ASK.2015.34.2.171

Text Independent Speaker Verficiation Using Dominant State Information of HMM-UBM  

Shon, Suwon (Department of Electronic Engineering Korea University)
Rho, Jinsang (Department of Electronic Engineering Korea University)
Kim, Sung Soo (Samsung Electronics)
Lee, Jae-Won (Samsung Electronics)
Ko, Hanseok (Department of Electronic Engineering Korea University)
Publication Information
The Journal of the Acoustical Society of Korea / v.34, no.2, 2015 , pp. 171-176 More about this Journal
Abstract
We present a speaker verification method by extracting i-vectors based on dominant state information of Hidden Markov Model (HMM) - Universal Background Model (UBM). Ergodic HMM is used for estimating UBM so that various characteristic of individual speaker can be effectively classified. Unlike Gaussian Mixture Model(GMM)-UBM based speaker verification system, the proposed system obtains i-vectors corresponding to each HMM state. Among them, the i-vector for feature is selected by extracting it from the specific state containing dominant state information. Relevant experiments are conducted for validating the proposed system performance using the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) database. As a result, 12 % improvement is attained in terms of equal error rate.
Keywords
Text-independent; Speaker verification; HMM-UBM; i-vectors;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, "Front-end factor analysis for speaker verification," IEEE Trans. on Audio, Speech, and Lang. Process. 19, 788-798 (2011).   DOI   ScienceOn
2 P. Kenny, "Bayesian speaker verification with heavy tailed priors," Odyssey Speker and Language Recognition Workshop, Brno, Czech Republic, (2010).
3 T. H. Kwon and H. S. Ko, "Performance improvement in speech recognition by weighting HMM likelihood" (in Korean), J. Acoust. Soc. Kr. 22, 145-152, 2003.
4 D. a. Reynolds, T. F. Quatieri, and R. B. Dunn, "Speaker verification using adapted Gaussian mixture models," Digital Signal Processing 10, 19-41 (2000).   DOI   ScienceOn
5 A. Poritz, "Linear predictive hidden Markov models and the speech signal," ICASSP, 1291-1294 (1982).
6 N. Z. Tishby, "On the application of mixture AR hidden markov models to text independent speaker recognition," IEEE Transactions on Signal Processing 39, 563-570 (1991).   DOI
7 T. Matsui, and S. Furui, "Comparison of text-independent speaker recognition methods using VQ-distortion and discrete/continuous HMM's," IEEE Transactions on Speech and Audio Processing 2, 1992-1995 (1994).
8 M. F. BenZeghiba, and H. Bourlard, "User-customized password speaker verification using multiple reference and background models," Speech Communication 48, 1200-1213 (2006).   DOI   ScienceOn
9 R. Gajsek, F. Mihelic, and S. Dobrisek, "Speaker state recognition using an HMM-based feature extraction method," Computer Speech & Language 27, 135-150 (2013).   DOI   ScienceOn
10 P. Kenny, "Joint factor analysis of speaker and session variability: Theory and algorithms," CRIM, Montreal, (Report) CRIM-06/08-13, 1-17 (2005).
11 P. Kenny, G. Boulianne, and P. Dumouchel, "Eigenvoice modeling with sparse training data," IEEE Transactions on Speech and Audio Processing 13, 345-354 (2005).   DOI   ScienceOn
12 L. R. Rabiner, "A tutorial on hidden markov-models and selected applications in speech recognition," Proceedings of the Ieee 77, 257-286 (1989).   DOI   ScienceOn
13 D. Garcia-romero, and C. Y. Espy-wilson, "Analysis of i-vector length normalization in speaker recognition systems.," Interspeech, 249-252 (2011).
14 J. Pelecanos, and S. Sridharan, "Feature warping for robust speaker verification," Interspeech, 213-218 (2001).