Automatic Pronunciation Assessment - A Review

Yassine El Kheir; Ahmed Ali; Shammur Absar Chowdhury

Automatic Pronunciation Assessment - A Review

Yassine El Kheir, Ahmed Ali, Shammur Absar Chowdhury

Hamad bin Khalifa University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics
Subtitle of host publication	EMNLP 2023
Publisher	Association for Computational Linguistics (ACL)
Pages	8304-8324
Number of pages	21
ISBN (Electronic)	9798891760615
Publication status	Published - 2023
Externally published	Yes
Event	2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore Duration: 6 Dec 2023 → 10 Dec 2023

Publication series

Name	Findings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference	2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/Territory	Singapore
City	Singapore
Period	6/12/23 → 10/12/23

Cite this

@inproceedings{c0811b9a3d3e43069de7e6a9ec0c4998,

title = "Automatic Pronunciation Assessment - A Review",

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.",

author = "{El Kheir}, Yassine and Ahmed Ali and Chowdhury, {Shammur Absar}",

note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

year = "2023",

language = "English",

series = "Findings of the Association for Computational Linguistics: EMNLP 2023",

publisher = "Association for Computational Linguistics (ACL)",

pages = "8304--8324",

booktitle = "Findings of the Association for Computational Linguistics",

address = "United States",

}

El Kheir, Y, Ali, A & Chowdhury, SA 2023, Automatic Pronunciation Assessment - A Review. in Findings of the Association for Computational Linguistics: EMNLP 2023. Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics (ACL), pp. 8304-8324, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, Singapore, 6/12/23.

Automatic Pronunciation Assessment - A Review. / El Kheir, Yassine; Ali, Ahmed; Chowdhury, Shammur Absar.
Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 8304-8324 (Findings of the Association for Computational Linguistics: EMNLP 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ali, Ahmed

AU - Chowdhury, Shammur Absar

PY - 2023

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AB - 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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M3 - Conference contribution

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BT - Findings of the Association for Computational Linguistics

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Y2 - 6 December 2023 through 10 December 2023

ER -

Automatic Pronunciation Assessment - A Review

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