Speech recognition using dynamic time warping with neural network trained templates

A dynamic time warping based speech recognition system with neural network trained templates is proposed. The algorithm for training the templates is derived based on minimizing classification error of the speech classifier. A speaker-independent isolated digit recognition experiment is conducted and achieves a 0.89% average recognition error rate with only one template for each digit, indicating that the derived templates are able to capture the speaker-invariant features of speech signals. Both nondiscriminative and discriminative versions of the neural net template training algorithm are considered. The former is based on maximum likelihood estimation. The latter is based on minimizing classification error. It is demonstrated through experiments that the discriminative training algorithm is far superior to the nondiscriminative one, providing both smaller recognition error rate and greater discrimination power. Experiments using different feature representation schemes are considered. It is demonstrated that the combination of the feature vector and the delta feature vector yields the best recognition result