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arXiv · 2101.06396

Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling

Daniel Korzekwa·Jaime Lorenzo-Trueba·Szymon Zaporowski·Shira Calamaro·Thomas Drugman·Bozena Kostek
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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.

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BibTeXRIS

Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski, Shira Calamaro, Thomas Drugman, Bozena Kostek. 2021-01-16. Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling. https://arxiv.org/abs/2101.06396

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