Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916235435835392 |
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| author | Salameh, Raghad Mdfaa, Mohamad Al Askarbekuly, Nursultan Mazzara, Manuel |
| author_facet | Salameh, Raghad Mdfaa, Mohamad Al Askarbekuly, Nursultan Mazzara, Manuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers Salameh, Raghad Mdfaa, Mohamad Al Askarbekuly, Nursultan Mazzara, Manuel Sound Artificial Intelligence Audio and Speech Processing 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. |
| title | Quranic Audio Dataset: Crowdsourced and Labeled Recitation from Non-Arabic Speakers |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.02675 |