Crowdsourced and Labeled Quranic Audio Dataset from Non-Arabic Speakers

Core Concepts

A crowdsourced dataset of Quranic recitations from non-Arabic speakers was created and annotated to support the development of AI models for learning Quranic recitation.

Abstract

The researchers explored the feasibility of crowdsourcing a carefully annotated Quranic audio dataset that can be used to build AI models to simplify the learning process for non-Arabic speakers. They used a volunteer-based crowdsourcing approach to collect audio recitations from 1287 participants across 11 non-Arabic countries, resulting in around 7000 Quranic recitations.
To annotate the collected audio data, the researchers developed a crowdsourcing platform called "Quran Voice". Proficient reciters were asked to listen to the audio recordings and provide labels on the correctness of the recitation, including categories like "Correct", "Has Mistakes", "Incomplete Verse", "Different Verse", "Multiple Verses", and "Empty/Not Related".
The annotation process 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 expert judgments. The researchers discuss the challenges faced in getting proficient reciters to participate in the annotation task and propose solutions to improve the process in the future.

Stats

Around 7000 Quranic recitations were collected from 1287 participants across 11 non-Arabic countries.
1166 recitations were annotated with 6 categories by 71 participants.
The crowd accuracy was 0.77, the inter-rater agreement was 0.63, and the agreement between the algorithm and expert labels was 0.89.

Quotes

"A dataset of Quranic recitation audios can be crowdsourced from beginner learners via a recitation app."
"The collected dataset can be labeled through a dedicated crowdsourcing tool."

Key Insights Distilled From

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

by Raghad Salam... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02675.pdf

Deeper Inquiries

How can the crowdsourcing process be further improved to increase participation and annotation quality from proficient reciters?

To enhance the crowdsourcing process and encourage more participation from proficient reciters while maintaining annotation quality, several strategies can be implemented:

Clear Instructions and Training: Provide detailed and easy-to-understand instructions for the annotation tasks. Conduct thorough training sessions to ensure that participants fully comprehend the task requirements and criteria for labeling.

Feedback Mechanism: Implement a feedback mechanism where participants receive constructive feedback on their annotations. This can help them improve their skills and accuracy in labeling Quranic recitations.

Incentives and Recognition: Offer incentives such as certificates, badges, or rewards for proficient reciters who consistently provide high-quality annotations. Recognizing their contributions can motivate them to continue participating.

Community Engagement: Foster a sense of community among participants by creating forums or discussion groups where they can interact, share experiences, and support each other. This can help in retaining participants and building a dedicated community of proficient annotators.

Quality Control Measures: Implement strict quality control measures, including regular checks on annotations, calibration tasks, and inter-rater agreement assessments. This ensures the accuracy and consistency of annotations provided by proficient reciters.

User-Friendly Platform: Ensure that the crowdsourcing platform is user-friendly, intuitive, and accessible across different devices. A seamless and efficient platform experience can attract more participants and improve overall annotation quality.

How can the Quranic audio dataset be leveraged to develop more advanced AI models for Quranic recitation learning and analysis beyond just detecting mistakes?

The Quranic audio dataset can serve as a valuable resource for developing advanced AI models that go beyond just detecting mistakes in recitations. Some techniques and approaches to leverage this dataset for more comprehensive Quranic recitation learning and analysis include:

Tajweed Rules Integration: Integrate Tajweed rules into the AI models to provide feedback on the correct application of Tajweed rules during recitation. This can help learners improve their pronunciation and adherence to the rules of Quranic recitation.

Personalized Learning: Develop AI models that offer personalized learning experiences based on individual reciter's strengths and weaknesses. This could involve adaptive learning algorithms that tailor feedback and practice exercises to each learner's specific needs.

Emotional Analysis: Incorporate sentiment analysis and emotional recognition techniques to assess the emotional delivery of recitations. AI models could provide feedback on the emotional impact and sincerity of recitations, enhancing the overall recitation experience.

Advanced Feedback Mechanisms: Implement AI models that provide detailed feedback on intonation, rhythm, and fluency in recitations. These models can offer specific suggestions for improvement and track progress over time.

Interactive Learning Tools: Develop interactive learning tools that use the audio dataset to create engaging and immersive learning experiences. This could include virtual recitation tutors, interactive quizzes, and gamified learning modules.

Semantic Analysis: Utilize natural language processing techniques to analyze the semantic content of recitations. AI models could identify themes, topics, and linguistic patterns in Quranic recitations, offering insights for deeper understanding and interpretation.

By exploring these advanced approaches, the Quranic audio dataset can be instrumental in developing AI models that not only detect mistakes but also enhance the overall learning, analysis, and appreciation of Quranic recitations.