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pretty_name: Quranic Audio Dataset - Crowdsourced and Labeled Recitation from Non-Arabic Speakers
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### Citation Information
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```
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@inproceedings{commonvoice:2020,
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pretty_name: Quranic Audio Dataset - Crowdsourced and Labeled Recitation from Non-Arabic Speakers
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# Dataset Card for Quranic Audio Dataset : Crowdsourced and Labeled Recitation from Non-Arabic Speakers
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### Dataset Summary
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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.
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In particular, we use the volunteer-based crowdsourcing genre and implement a crowdsourcing API to gather audio assets.
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We developed a crowdsourcing platform called Quran Voice for annotating the gathered audio assets.
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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.
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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.
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## How to use
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## Dataset Structure
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### Data Instances
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### Data Fields
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### Citation Information
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```
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@inproceedings{commonvoice:2020,
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author = {Salameh, R., Mdfaa, M. A., Askarbekuly, N., & Mazzara, M.},
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title = {Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers},
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year = 2024,
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eprint = {2405.02675},
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eprinttype = {arxiv},
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url = {https://arxiv.org/abs/2405.02675},
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language = {english},
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booktitle = {},
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pages = {}
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}
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```
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