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Discriminative training of dynamic programming based speech recognizers

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2 Author(s)
P. C. Chang ; Telecommun. Labs., Minist. of Commun., Taiwan ; B. H. Juang

A new minimum recognition error formulation and a generalized probabilistic descent (GPD) algorithm are analyzed and used to accomplish discriminative training of a conventional dynamic-programming-based speech recognizer. The objective of discriminative training here is to directly minimize the recognition error rate. To achieve this, a formulation that allows controlled approximation of the exact error rate and renders optimization possible is used. The GPD method is implemented in a dynamic-time-warping (DTW)-based system. A linear discriminant function on the DTW distortion sequence is used to replace the conventional average DTW path distance. A series of speaker-independent recognition experiments using the highly confusible English E-set as the vocabulary showed a recognition rate of 84.4% compared to ~60% for traditional template training via clustering. The experimental results verified that the algorithm converges to a solution that achieves minimum error rate

Published in:

IEEE Transactions on Speech and Audio Processing  (Volume:1 ,  Issue: 2 )