Variational Bayesian GMM for speech recognition

EUROSPEECH 2003 - 8th European Conference on Speech Communication and Technology

Volumn , Issue , 2003, Pages 441-444

  Valente, Fabio ^a   Wellekens, Christian ^a  

^a EURECOM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

SPEECH COMMUNICATION;

GAUSSIAN MIXTURE MODEL; MAP LEARNING; MODEL SELECTION; OVERFITTING; PHONEME RECOGNITION; SINGULAR SOLUTIONS; TIMIT DATABASE; VARIATIONAL BAYESIAN;

SPEECH RECOGNITION;

EID: 85009174866     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Attias, H., "A Variational Bayesian framework for graphical models", Advances in Neural Information Processing Systems 12, MIT Press,Cambridge, 2000.
2. Bishop C.M., Winn J. "Structured variational distribution in VIBES" ,Proceedings Artificial Intelligence and Statistics, Key West, Florida, 2003 .
3. Corduneau A., Bishop C.M. "Variational Bayesianmodel selection for mixture distributions", In T. Richardson and T. Jaakkola (Eds.), Proc. 8th Int. Conf. on Artificial Intelligence and Statistics, pp. 27-34. Morgan Kaufmann.
4. Watanabe S. et al. "Application of the Variational Bayesian approach to speech recognition" NIPS'02. MIT Press.
5. O.-W. Kwon, T.-W. Lee, K. Chan, "Application of variational Bayesian PCA for speech feature extraction," Proc. ICASSP 2002, Orlando, FL, pp. I-825-I-828, May 2002.
6. Somervuo P., "Speech modeling using Variational Bayesianmixture of gaussians", Proc ICSLP 2002.
7. MacKay D.J.C. "Local Minima, symmetry breaking and model pruning in variational free energy minimization"