Boosting Gaussian mixture models via discriminant analysis

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2008, Pages

  Tang, Hao ^a   Huang, Thomas S ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRIMINANT ANALYSIS; GAUSSIAN DISTRIBUTION; OBJECT RECOGNITION;

ARBITRARY PROBABILITY DISTRIBUTION; EMOTION RECOGNITION; FEATURE DIMENSIONS; FEATURE REPRESENTATION; GAUSSIAN MIXTURE MODEL; HIGH DIMENSIONALITY; MINIMUM ERROR RATE; TRAINING CONDITIONS;

SPEECH RECOGNITION;

EID: 77957961838     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icpr.2008.4761791     Document Type: Conference Paper

References (12)

1. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
2. Douglas A. Reynolds. Speaker identification and verfication using Gaussian mixture speaker models. Speech Communication, Vol. 17, I. 1-2, pp. 91.108, 1995.
3. H. Ning, W. Xu, Y. Gong, T. Huang. Improving Speaker Diarization by Cross EM Refinement. ICME, 2006.
4. Lawrence Rabiner, Biing-Hwang Juang. Fundamentals of Speech Recognition. Prentice Hall, 1993.
5. H.H. Permuter, J. Francos and I.H. Jarmyn. Gaussian mixture models of texture and colour for image database retrieval. ICASSP, 2003.
6. H. Greenspan, A. Ruf, J. Goldberger. Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Transactions on Medical Imaging, Vol. 25, I. 9, pp. 1233.1245, 2006.
7. D. A. Reynolds, T. F. Quatieri and R. B. Dunn. Speaker Verification using Adapted Gaussian Mixture Models. Digital Signal Processing, vol. 10, no. 1-3, 2000.
8. Arthur Dempster, Nan Laird, and Donald Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. of Royal Stat. Society, B, 39(1):1C38, 1977.
9. Richard O. Duda, Peter E. Hart and David G. Stork. Pattern Classfication (2nd Ed.). Wiley-Interscience, 2000.
10. M. Figueiredo, A. Jain. Unsupervised Learning of Finite Mixture Models. PAMI, 24(3), pp. 381.396, 2002.
11. G. Celeux, S. Chretien, F. Forbes, and A. Mkhadri. A component-wise EM algorithm for mixtures. Tech. Rep. 674, INRIA, Rhone-Alpes, France.
12. Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences, no. 55. 1997.