Sciweavers

Share
ICML
2009
IEEE

Matrix updates for perceptron training of continuous density hidden Markov models

12 years 7 months ago
Matrix updates for perceptron training of continuous density hidden Markov models
In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automatic speech recognition use multivariate Gaussian emission densities (or mixtures thereof) parameterized in terms of their means and covariance matrices. For discriminative training of CDHMMs, we reparameterize these Gaussian distributions in terms of positive semidefinite matrices that jointly encode their mean and covariance statistics. We show how to explore the resulting parameter space in CD-HMMs with perceptron-style updates that minimize the distance between Viterbi decodings and target transcriptions. We experiment with several forms of updates, systematically comparing the effects of different matrix factorizations, initializations, and averaging schemes on phone accuracies and convergence rates. We present experimental results for context-independent CD-HMMs trained in this way on the TIMIT speech cor...
Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul
Comments (0)
books