Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling
Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski, Shira Calamaro, Thomas Drugman, Bozena Kostek
Abstract
A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker. This approach makes two simplifying assumptions: a) phonemes can be recognized from speech with high accuracy, b) there is a single correct way for a sentence to be pronounced. These assumptions do not always hold, which can result in a significant amount of false mispronunciation alarms. We propose a novel approach to overcome this problem based on two principles: a) taking into account uncertainty in the automatic phoneme recognition step, b) accounting for the fact that there may be multiple valid pronunciations. We evaluate the model on non-native (L2) English speech of German, Italian and Polish speakers, where it is shown to increase the precision of detecting mispronunciations by up to 18% (relative) compared to the common approach.
BibTeX
@inproceedings{icassp2021_mispronunciation,
title = {Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling},
author = {Daniel Korzekwa and Jaime Lorenzo-Trueba and Szymon Zaporowski and Shira Calamaro and Thomas Drugman and Bozena Kostek},
booktitle = {ICASSP 2021},
year = {2021}
}