This work presents methods of assessing non-native speech to aid computer-assisted pronunciation teaching. These methods are based on automatic speech recognition (ASR) techniques using Hidden Markov Models. Confidence scores at the phoneme level are calculated to provide detailed information about the pronunciation quality of a foreign language student. Experimental results are given based on both artificial data and a database of non-native speech, the latter being recorded specifically for this purpose. The presented results demonstrate the metrics' capability to locate and assess mispronunciations at the phoneme level.
Cite as: Witt, S., Young, S.J. (1997) Language learning based on non-native speech recognition. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 633-636, doi: 10.21437/Eurospeech.1997-227
@inproceedings{witt97_eurospeech,
author={Silke Witt and Steve J. Young},
title={{Language learning based on non-native speech recognition}},
year=1997,
booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
pages={633--636},
doi={10.21437/Eurospeech.1997-227}
}