@inproceedings{kheir-etal-2023-automatic,
title = "Automatic Pronunciation Assessment - A Review",
author = "El Kheir, Yassine and
Ali, Ahmed and
Chowdhury, Shammur Absar",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.557/",
doi = "10.18653/v1/2023.findings-emnlp.557",
pages = "8304--8324",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Pronunciation Assessment - A Review
%A El Kheir, Yassine
%A Ali, Ahmed
%A Chowdhury, Shammur Absar
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kheir-etal-2023-automatic
%X 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.
%R 10.18653/v1/2023.findings-emnlp.557
%U https://aclanthology.org/2023.findings-emnlp.557/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.557
%P 8304-8324
Markdown (Informal)
[Automatic Pronunciation Assessment - A Review](https://aclanthology.org/2023.findings-emnlp.557/) (El Kheir et al., Findings 2023)
ACL
- Yassine El Kheir, Ahmed Ali, and Shammur Absar 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.