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

2025/06/09 by Yassine El Kheir, Omnia Ibrahim, Kheir, Yassine El +27 · 2 citations
Computer Science · Psychology · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #Phonetics and Phonology Research #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.07722

openalex publication_date 2025/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic
• mHuBERT-147: A Compact Multilingual HuBERT Model
• A Large-Scale Evaluation of Speech Foundation Models
• Quran Recitation Recognition using End-to-End Deep Learning
• SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation
• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
• HuBERT: Self-Supervised Speech Representation Learning by Masked\n Prediction of Hidden Units
• SUPERB: Speech processing Universal PERformance Benchmark
• wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
• Common Voice: A Massively-Multilingual Speech Corpus
• Phone-level pronunciation scoring and assessment for interactive language learning

Cited by

• Enhancing Quranic Learning: A Multimodal Deep Learning Approach for Arabic Phoneme Recognition
• Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning

Related

• Enhancing Quranic Learning: A Multimodal Deep Learning Approach for Arabic Phoneme Recognition
• Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning
• Mispronunciation Detection of Basic Quranic Recitation Rules using Deep Learning
• An ensemble-based framework for mispronunciation detection of Arabic phonemes
• ASMDD: Arabic Speech Mispronunciation Detection Dataset