Abstract
Chinese word segmentation is an important and fundamental task. A recent advance in Chinese word segmentation is using a specialized Hidden Markov Model, called M-HMM, based on BIES, labels of the position of a constituent character in a word. The main purpose of this thesis is to see if the M-HMM will improve the pronunciation annotation. Firstly, a character sequence (sentence without word boundary mark－space) is segmented into word sequence, and secondly, the words are transformed into pronunciation annotation. Our experiment shows that M-HMM does help. As a third stage, we apply some transformation rules to further improve the correctness of the pronunciation annotation.