Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers

TL;DR

This paper presents a crowdsourced, annotated Quranic audio dataset from non-Arabic speakers, enabling AI-based recitation learning tools, with detailed collection, annotation, and accuracy metrics.

Contribution

It introduces a novel crowdsourcing platform and dataset for Quranic recitations from non-Arabic speakers, facilitating AI development for recitation learning.

Findings

Collected 7000 recitations from 1287 participants across 11 countries

Achieved a crowd accuracy of 0.77 and inter-rater agreement of 0.63

Labeling accuracy with algorithm and expert comparison is 0.89

Abstract

This paper addresses the challenge of learning to recite the Quran for non-Arabic speakers. We explore the possibility of crowdsourcing a carefully annotated Quranic dataset, on top of which AI models can be built to simplify the learning process. In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets. We integrated the API into an existing mobile application called NamazApp to collect audio recitations. We developed a crowdsourcing platform called Quran Voice for annotating the gathered audio assets. As a result, we have collected around 7000 Quranic recitations from a pool of 1287 participants across more than 11 non-Arabic countries, and we have annotated 1166 recitations from the dataset in six categories. We have achieved a crowd accuracy of 0.77, an inter-rater agreement of 0.63 between the annotators, and 0.89 between the labels assigned by the algorithm and the expert judgments.