Abstract
Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.- Anthology ID:
- 2023.findings-emnlp.557
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8304–8324
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.557
- DOI:
- 10.18653/v1/2023.findings-emnlp.557
- Cite (ACL):
- Yassine Kheir, Ahmed Ali, and Shammur Chowdhury. 2023. Automatic Pronunciation Assessment - A Review. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8304–8324, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Pronunciation Assessment - A Review (Kheir et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.findings-emnlp.557.pdf