Ayah Alignment Coverage Eval

ayah-alignment-coverage-eval

Tadabur: A Large-Scale Quran Audio Dataset — Alherran (2026) (arXiv:2604.18932, 2026)

What this evaluates

Evaluates the ability of an audio segmentation pipeline to correctly identify and align individual Quranic verses (ayahs) from long-form recitations. It probes the robustness of alignment methods and ASR backbones against recitation style variations and phonological differences.

Datasets

• Tadabur Evaluation Set (5 Reciters) — total ?; splits: test (-1); repo https://github.com/fherran/tadabur

Metrics

• Alignment Coverage (%) (primary) — range: percent
  • Percentage of ayahs successfully identified and segmented by the pipeline: (Aligned Ayahs / Total Ayahs) × 100.

Input / output format

Input: Long-form audio recordings of complete Quran recitations from five specific reciters, paired with the canonical Quranic text for alignment.

Output: A binary alignment decision (aligned/not aligned) for each ayah in the canonical text, resulting in segmented audio files for successfully aligned verses.

Scoring recipe

def compute_alignment_coverage(predictions, gold_ayahs):
    aligned_count = sum(1 for pred in predictions if pred.is_aligned)
    total_count = len(gold_ayahs)
    coverage_pct = (aligned_count / total_count) * 100
    return coverage_pct

Common pitfalls

• Coverage scores can be artificially inflated if the evaluation audio contains repeated ayahs; the protocol requires strict deduplication.
• ASR transcription quality directly dictates alignment success, meaning low coverage may reflect poor speech recognition rather than a flawed alignment algorithm.

Evidence (verbatim from paper)

We define alignment coverage as the percentage of ayahs successfully identified and segmented by the pipeline: Coverage = (Aligned Ayahs / Total Ayahs) × 100. The evaluation set for each reciter was curated specifically for this purpose: recordings were gathered independently, deduplicated, and cleaned to ensure that each ayah appears exactly once.

Citation

@misc{alherran2026tadabur,
  title={Tadabur: A Large-Scale Quran Audio Dataset},
  author={Alherran (2026)},
  year={2026},
  note={arXiv:2604.18932}
}

• arXiv: 2604.18932