Automatic Speech Recognition (ASR) can be very useful in language learning tools in order to correct mistakes in the pronunciation of foreign words by non-native speakers. Most of the systems integrating ASR proposed on the market are just rejecting or accepting whole words or whole sentences. In this paper, we propose a method to identify the pronunciation errors at the phoneme level. Indeed, mistakes are often predictable and concern a particular subset of phonemes not present in the mother language of the speaker. We describe two different approaches based on the Hybrid HMM/ANN technology. The methodology for the training of the recognizer is discussed, and we describe a new approach where a mixed database is used to train a speech recognition system able to detect pronunciation errors at the phoneme level. Preliminary but promising results have been obtained on the DEMOSTHENES database.
Cite as: Deroo, O., Ris, C., Gielen, S., Vanparys, J. (2000) Automatic detection of mispronounced phonemes for language learning tools. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 681-684, doi: 10.21437/ICSLP.2000-169
@inproceedings{deroo00_icslp,
author={Olivier Deroo and Christophe Ris and Sofie Gielen and Johan Vanparys},
title={{Automatic detection of mispronounced phonemes for language learning tools}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 1, 681-684},
doi={10.21437/ICSLP.2000-169}
}