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Transformer models show improved accuracy for Quranic ASR

By PulseAugur Editorial · [1 sources] ·

Researchers have conducted a comparative study on pretrained Transformer models for Quranic Automatic Speech Recognition (ASR), aiming to reduce high Word Error Rates (WER) on user-recited verses. The study fine-tuned models like Wav2Vec2.0, HuBERT, and XLS-R on an 870-hour Quranic dataset, identifying key factors for transcription accuracy. The best configuration achieved a WER of 0.08 on the EveryAyah subset, a significant improvement over the Citrinet baseline, while also reducing training time. AI

IMPACT Improves accuracy and efficiency for specialized ASR tasks, potentially aiding Quranic study and accessibility.

RANK_REASON Academic paper detailing a comparative study of AI models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Transformer models show improved accuracy for Quranic ASR

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Academic paper detailing a comparative study of AI models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nabil Mosharraf Hossain (Greentech Apps Foundation, United Kingdom), Riasat Islam (Greentech Apps Foundation, United Kingdom, Queen Mary University of London, United Kingdom), Unaizah Obaidellah (University of Malaya, Malaysia) ·

    A Comparative Study of Pretrained Transformer Models for Quranic ASR: Speech Representations, Label Formats, and Dataset Composition

    arXiv:2606.19747v1 Announce Type: new Abstract: Quran Automatic Speech Recognition (ASR) aims to convert Quranic recitation into text, enabling applications such as aided memorisation tools and Quranic search engines. However, existing ASR models often exhibit high Word Error Rat…

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