HMM-Based speech recognition using multi-dimensional multi-labeling

This paper describes a new vector quantization (VQ; so-called labeling) method of a speech recognition system based on hidden Markov model (HMM). For improving the VQ accuracy in a simple manner, "multi-labeling" which generates multiple labels at each frame was introduced while keeping a conventional HMM formulation. Furthermore, in order to represent characteristics of speech accurately and effectively, "multi-dimensional labeling" was also introduced which quantizes multiple features such as spectral dynamics and spectrum independently. This labeling method was tested in an isolated word recognition task using 150 Japanese confusable words. The recognition error rate was roughly reduced to 1/2 or less compared with the conventional method.