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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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Authors (6)
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Daniel Korzekwa mgr inż.
- Amazon
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Jaime Lorenzo-trueba
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Shira Calamaro
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Thomas Drugman dr
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Korzekwa D., Lorenzo-Trueba J., Zaporowski S., Calamaro S., Drugman T., Kostek B.: Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling// / : , 2021,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/icassp39728.2021.9413953
- Verified by:
- Gdańsk University of Technology
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