Year: 2008
Type: conference-paper
Abstract: Text normalization is an important component in mandarin Text-to-Speech system.This paper develops a taxonomy of Non-Standard Words (NSW's) based on a Large-scale Chinese corpus and proposes a three-stage text normalization strategy: Finite State Automata (FSA) for initial classification, Maximum Entropy (ME) Classifier & Rules for further classification and General Rules for standard word conversion.The three-stage approach achieves Precision of 96.02% in experiments, 5.21% higher than that of simple rule based approach and 2.21% higher than that of simple machine learning method.Experiments results show that the approach of three-stage disambiguation strategy for text normalization makes considerable improvement, and works well in real TTS system.
Authors: Tao Zhou, Yuan Dong, De-zhi Huang, Wu Liu, Hai-la Wang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4390913089