Isolated-word speech recognition using multi-section vector quantization code books

A new approach to isolated-word speech recognition using vector quantization (VQ) is examined. In this approach, words are recognized by means of sequences of VQ code books called multi-section code books. A separate multi-section code book is designed for each word in the recognition vocabulary by dividing the word into equal-length sections and designing a standard VQ code book for each section. Unknown words are classified by dividing them into corresponding sections, encoding them with the multi-section code books, and finding the multi-section code book that yields the smallest average distortion. For speaker-independent recognition of a 20-word vocabulary containing the digits, this approach achieves 95% recognition accuracy for the full vocabulary and 99% for the digits, in both causes with approximately 90% fewer distortion computations than typical dynamic-time-warping approaches. In addition, the approach achieves greater than 99% accuracy for speaker-dependent recognition of the digits with only 1 distortion computation per input frame per vocabulary word. The approach is described, detailed experimental results are presented and discussed, and computational requirements are analyzed.