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Automatic Pronunciation Assessment -- A Review

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arxiv 2310.13974 v1 pith:3YPD25A5 submitted 2023-10-21 cs.CL cs.SDeess.AS

Yassine El Kheir , Ahmed Ali , Shammur Absar Chowdhury This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords pronunciationassessmentreviewchallengesyearsapplicationautomaticavailable
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

    eess.AS 2025-08 conditional novelty 6.0 of 10

    The authors release a rule-based Quran Phonetic Script, an 890-hour expert recitation dataset, and a multi-head CTC model that achieves 0.16% average phoneme error rate on held-out reciters.

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