Effective online unsupervised adaptation of Gaussian mixture models and its application to speech classification

Pattern Recognition Letters

Volumn 29, Issue 6, 2008, Pages 735-744

  Zhang, Yongxin ^a   Scordilis, Michael S ^a  

^a University of Miami College of Engineering   (United States)

Author keywords

Gaussian mixture model; Online adaptation; Speech classification; Unsupervised adaptation

Indexed keywords

CLASSIFICATION (OF INFORMATION); MATHEMATICAL MODELS; ONLINE SYSTEMS; SPEECH RECOGNITION; UNSUPERVISED LEARNING;

GAUSSIAN MIXTURE MODELS; SPEECH CLASSIFICATION;

GAUSSIAN DISTRIBUTION;

EID: 39949083004     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2007.12.006     Document Type: Article

References (18)

1. Arifianto, D., Kobayashi, T., 2003. IFAS-based voiced/unvoiced classification of speech signal. Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 812-815.
2. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39 (1977) 1-38
3. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification and Scene Analysis (2000), John Wiley, New York
4. Gauvain J.L., and Lee C.H. Maximum a posterior estimation for multivariate gaussian mixture observation of Markov chains. IEEE Trans. Speech Signal Process. 2 (1994) 291-298
5. Jain A.K., Robert D., and Mao J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 2 (2002) 4-37
6. KIDS, 2001. .
7. Li Q., Zheng J., Tsai A., and Zhou Q. Robust endpoint detection and energy normalization for real-time speech and speaker recognition. IEEE Trans. Speech Audio Process. 10 3 (2002) 146-157
8. Mahgoub, Y.A., Dansereau, R.M., 2005. Voicing-state classification of co-channel speech using nonlinear state-space reconstruction. In: Proc. of the 2005 IEEE Internat. Conf. on Acoust., Speech, and Signal Process., pp. 409-412.
9. Neal R., and Hinton G. A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Jordan M. (Ed). Learning in Graphical Models (1998), Kluwer Academic Publishers, The Netherlands 355-371
10. Pwint, M., Sattar, F., 2005. A new speech/non-speech classification method using minimal Walsh basis functions. In: Proc. of the 2005 IEEE Internat. Symp. on Circuits Systems, pp. 2863-2866.
11. Ramirez J., Segura J.C., Benitez C., Garcia L., and Rubio A. Statistical voice activity detection using a multiple observation likelihood ratio test. IEEE Signal Process. Lett. 12 10 (2005) 689-692
12. Reynolds D.A., Quatieri T.F., and Dunn R.B. Speaker verification using adapted Gaussian mixture models. Digital Signal Process. 10 (2000) 19-41
13. Shin, W.H., Lee, B.S, Lee, Y.K., Lee, J.S., 2000. Speech/non-speech classification using multiple features for robust endpoint detection. In: Proc. of the 2000 IEEE Internat. Conf. on Acoust., Speech, and Signal Process., pp. 1399-1402.
14. Siegel L., and Bessey A. Voiced/unvoiced/mixed excitation classification of speech. IEEE Trans. Acoust. Speech Signal Process. (1982) 451-460
15. Sohn J., Kim N.S., and Sung W. A statistical model-based voice activity detection. IEEE Signal Process. Lett. 6 19 (1999) 1-3
16. SR4X, 2002. .
17. TIMIT Corpus, 1990. The DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus, NIST Speech Disc 1-1.1, NTIS order number PB91-505065 (CD-ROM).
18. Wu B.F., and Wang K.C. Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments. IEEE Trans. Speech Audio Process. 13 5 (2005) 762-775