A STUDY ON RECOGNITION OF CONNECTED SPOKEN-CHINESE WORDS

This paper presents a pattern matching approach to connected-Chinese word recognition. First, a method of adaptive amplitude normalization is proposed to pre-process speech data, and a sound stimulus parameter is introduced to compress and normalize the speech data. Then, Using isolated word token as reference pattern, a fast dynamic time warping (FDTW) matching procedure is perbormed. Based on the FDTW algorithm, a simplified dynamic programming decision strategy is described. Finally, the speaker-dependent recognition results for Chinese digit strings (of unknown variable length from 2-5 digts) are given with the accuracy for digit string/ digit being 96.8/98.8 percent for the standard Chin se pronunciation, and 97.6/99.0 percent for the way of pronunciation used in telecommunication.