We present the findings of the first shared task on Qur’anic pronunciation assessment, which focuses on addressing the unique challenges of evaluating the precise pronunciation of Qur’anic recitation. To fill an existing research gap, the Iqra’Eval 2025 shared task introduces the first open benchmark for Mispronunciation Detection and Diagnosis (MDD) in Qur’anic recitation, using Modern Standard Arabic (MSA) reading of Qur’anic texts as its case study. The task provides a comprehensive evaluation framework with increasingly complex subtasks: error localization and detailed error diagnosis. Leveraging the recently developed QuranMB benchmark dataset along with auxiliary training resources, this shared task aims to stimulate research in an area of both linguistic and cultural significance while addressing computational challenges in pronunciation assessment.

Databases for studying speech rhythm and tempo exist for numerous languages. The present corpus was built to allow comparisons between Arabic speech rhythm and other languages. 10 Egyptian speakers (gender-balanced) produced speech in two different speaking styles (read and spontaneous). The design of the reading task replicates the methodology used in the creation of BonnTempo corpus (BTC). During the spontaneous task, speakers talked freely for more than one minute about their daily life and/or their studies, then they described the directions to come to the university from a famous near location using a map as a visual stimulus. For corpus annotation, the database has been manually and automatically time-labeled, which makes it feasible to perform a quantitative analysis of the rhythm of Arabic in both Modern Standard Arabic (MSA) and Egyptian dialect variety. The database serves as a phonetic resource, which allows researchers to examine various aspects of Arabic supra-segmental features and it can be used for forensic phonetic research, for comparison of different speakers, analyzing variability in different speaking styles, and automatic speech and speaker recognition.