An endpoint relaxation method for dynamic time warping algorithms

Inaccurate detection of the endpoints of the test and reference patterns is a major source of errors in discrete utterance recognition by dynamic time warping. If the vocabulary contains similar sounding words whose differences are at their beginnings or ends as in the alphadigit vocabulary, the error rate may greatly increase due to endpoint detection errors. Several methods to improve the recognition accuracy by relaxing or adjusting the endpoints have been suggested. They, however, do not work well in all cases and actually the error rate may increase. We propose a new method to compensate for endpoint detection errors and compare our improved method with two existing methods on the alphadigit vocabulary.