Discriminative template training for dynamic programming speech recognition

A newly proposed 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. Unlike many other approaches, the objective of discriminative training the new framework is to directly minimize the recognition error rate. A series of speaker independent recognition experiments using the highly confusing English E-set as the vocabulary was conducted to examine the characteristics of the GPD method for discriminative training. Without ad hoc supplementary schemes, the method achieved a recognition rate of 83.7%, a remarkable performance improvement compared to 63.8% with the traditional template training via clustering. The experimental results verify that the GPD algorithm with the new minimum recognition error formulation indeed converges to a solution that accomplishes the objective of minimum error rate