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

TL;DR

This paper introduces a comprehensive benchmark for Arabic pronunciation assessment using Qur'anic recitation, including data processing, a specialized phoneme set, and baseline evaluations to advance research in this area.

Contribution

It presents the first publicly available test set for Arabic mispronunciation detection and establishes a standardized framework for future pronunciation assessment research.

Findings

Baseline models show promising results but highlight challenges in MSA pronunciation assessment.

The Qur'anic Mispronunciation Benchmark (QuranMB.v1) provides a new resource for Arabic speech technology.

The framework supports future development of more accurate pronunciation evaluation tools.

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.