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New annotation method improves Quran recitation ASR error detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for annotating mistakes in Quran memorization transcripts generated by automatic speech recognition (ASR). This annotation process distinguishes between actual errors, repetitions, and accepted spelling variations. The developed evaluator scores these labels and their positions, achieving a label-aware F1 score of 0.525 and a localization F1 score of 0.826 with a plain diff. Preliminary tests with six coding agents and eight models showed varied performance, with most outperforming baseline methods, highlighting the importance of convention and normalization in ASR evaluation. AI

IMPACT This research could lead to more accurate evaluation of ASR systems for specialized domains like religious text memorization.

RANK_REASON The cluster contains an academic paper detailing a new annotation method for ASR transcripts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New annotation method improves Quran recitation ASR error detection

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The cluster contains an academic paper detailing a new annotation method for ASR transcripts. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mohamad Al Mdfaa, Nursultan Askarbekuly, Ahmed Helaly, Ubai Sandouk, Manuel Mazzara ·

    What Counts as a Mistake? Annotating Recitation Events in Quran Memorization Transcripts

    arXiv:2609.12085v1 Announce Type: new Abstract: Checking Quran recitation from an ASR transcript requires distinguishing unresolved mistakes from repetitions, repairs, opening formulas and accepted spelling differences. We report a completed human annotation of 100 production rec…

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