# Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study

arXiv: https://arxiv.org/abs/2506.07722 · HTML: https://mlsanity.com/paper/2506.07722/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning](https://mlsanity.com/paper/2509.00094/) (2025)
- [Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment](https://mlsanity.com/paper/2604.06191/) (2026)
- [Enhancing Quranic Learning: A Multimodal Deep Learning Approach for Arabic Phoneme Recognition](https://mlsanity.com/paper/2511.17477/) (2025)

## Abstract (arXiv)

We present a unified benchmark for mispronunciation detection in Modern Standard Arabic (MSA) using Qur'anic recitation as a case study. Our approach lays the groundwork for advancing Arabic pronunciation assessment by providing a comprehensive pipeline that spans data processing, the development of a specialized phoneme set tailored to the nuances of MSA pronunciation, and the creation of the first publicly available test set for this task, which we term as the Qur'anic Mispronunciation Benchmark (QuranMB.v1). Furthermore, we evaluate several baseline models to provide initial performance insights, thereby highlighting both the promise and the challenges inherent in assessing MSA pronunciation. By establishing this standardized framework, we aim to foster further research and development in pronunciation assessment in Arabic language technology and related applications.