Applying matrix quantization to isolated word recognition

A new approach to isolated word recognition is examined. This approach is based on an extension of vector quantization speech coding, called matrix quantization speech coding, that was developed by Tsao and Gray. In this new approach, a codebook containing a set of time-ordered-sequences of speech spectra represents each vocabulary word. A word is recognized by encoding it with each codebook and classifying the input word according to the codebook that yields the smallest distortion. On the digits, this approach achieved a speaker independent recognition accuracy greater than 98%. The approach is described, experimental results are presented, and comparisons with vector quantization based approaches are given.