DocumentCode :
661304
Title :
Predicting gradation of L2 English mispronunciations using ASR with extended recognition network
Author :
Hao Wang ; Meng, Hsiang-Yun ; Xiaojun Qian
Author_Institution :
Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Hong Kong, China
fYear :
2013
fDate :
Oct. 29 2013-Nov. 1 2013
Firstpage :
1
Lastpage :
4
Abstract :
A CAPT system can be pedagogically improved by giving effective feedback according to the severity of mispronunciations. We obtained perceptual gradations of L2 English mispronunciations through crowdsourcing, conducted quality control to filter for reliable ratings and proposed approaches to predict gradation of word-level mispronunciations. This paper presents our work on making improvements using ASR with extended recognition network to the previous predicting approach to solve its limitations: 1. it is not working for those mispronounced words whose transcriptions are not immediately available; 2. perceptually differently articulated words with the same transcription have the same predicted gradation.
Keywords :
computer based training; natural language processing; quality control; speech recognition; ASR; CAPT system; L2 English mispronunciations; computer-assisted pronunciation training; crowdsourcing; extended recognition network; gradation prediction; quality control; word-level mispronunciations; Linear regression; Manuals; Reliability; Speech; Speech recognition; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific
Conference_Location :
Kaohsiung
Type :
conf
DOI :
10.1109/APSIPA.2013.6694165
Filename :
6694165
Link To Document :
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