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

arXiv: https://arxiv.org/abs/2405.02675 · HTML: https://mlsanity.com/paper/2405.02675/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 1 of 1)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)

## Built on by (most cited first, 4 of 4)

- [From RAG to Agentic RAG for Faithful Islamic Question Answering](https://mlsanity.com/paper/2601.07528/) (2026)
- [A Critical Review of the Need for Knowledge-Centric Evaluation of Quranic Recitation](https://mlsanity.com/paper/2510.12858/) (2025)
- [Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran](https://mlsanity.com/paper/2601.17880/) (2026)
- [What Counts as a Mistake? Annotating Recitation Events in Quran Memorization Transcripts](https://mlsanity.com/paper/2609.12085/) (2026)

## Abstract (arXiv)

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.