Please use this identifier to cite or link to this item:
Title: Speech synthesis for error training models in CALL
Authors: Zhang, X
Lu, Q 
Wan, J
Ma, G
Chiu, TS
Ye, W
Zhou, W
Li, Q
Keywords: Training data preparation
Computer aided language learning
Speech synthesis formant modification
Issue Date: 2009
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5459, p. 260-269 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: A computer assisted pronunciation teaching system (CAPT) is a fundamental component in a computer assisted language learning system (CALL). A speech recognition based CAPT system often requires a large amount of speech data to train the incorrect phone models in its speech recognizer. But collecting incorrectly pronounced speech data is a labor intensive and costly work. This paper reports an effort on training the incorrect phone models by making use of synthesized speech data. A special formant speech synthesizer is designed to filter the correctly pronounced phones into incorrect phones by modifying the formant frequencies. In a Chinese Putonghua CALL system for native Cantonese speakers to learn Mandarin, a small experimental CAPT system is built with a synthetic speech data trained recognizer. Evaluation shows that a CAPT system using synthesized data can perform as good as or even better than that using real data provided that the size of the synthetic data are large enough.
Description: 22nd International Conference on the Computer Processing of Oriental Languages (ICCPOL2009), Language Technology for the Knowledge-based Economy, Hong Kong, 26-26 March, 2009
ISBN: 978-3-642-00830-6
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-00831-3_24
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.