Text normalization in mandarin Text-to-Speech system

Text normalization is an important component in Text-to-Speech system and the difficulty in text normalization is to disambiguate the Non-Standard Words (NSWs). This paper develops a taxonomy of NSWs on the basis of a large scale Chinese corpus, and proposes a two-stage NSWs disambiguation strategy, Finite State Automata (FSA) for initial classification and Maximum Entropy (ME) classifiers for subclass disambiguation. Based on the above NSWs taxonomy, the two-stage approach achieves an F-score of 98.53% in open test, 5.23% higher than that of FSA based approach. Experiments show that the NSWs taxonomy ensures FSA a high baseline performance and ME classifiers make considerable improvement, and the two-stage approach adapts well to new domains.