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        <title>Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content</title>
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        <namePart type="given">Reihaneh</namePart>
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    <abstract>Automatic Speech Recognition (ASR) transcription accuracy remains highly sensitive to audio segmentation strategies, yet most benchmarks assume oracle timestamps unavailable in deployment. We systematically evaluate how audio segmentation affects Whisper’s performance on 10 hours of Persian YouTube content, comparing transcript-aligned (oracle) versus silence-based (realistic) approaches across contrasting acoustic conditions. Results reveal striking content-type dependency: podcast content benefits from timestamp segmentation (33% lower mean WER), while entertainment content favors silence-based segmentation (8% lower mean WER). This finding demonstrates that optimal segmentation must be content-aware, with silence detection better capturing natural boundaries in acoustically heterogeneous media while avoiding mid-utterance splits. We publicly release our evaluation framework, 10 hours of audio with gold transcripts, and segmentation results here: https://github.com/ri164-bolleit/persian-youtube-whisper-benchmark</abstract>
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%0 Conference Proceedings
%T Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content
%A Iranmanesh, Reihaneh
%A Ziaei, Rojin
%A Garman, Joe
%Y Merchant, Rayyan
%Y Megerdoomian, Karine
%S The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-371-5
%F iranmanesh-etal-2026-segmentation
%X Automatic Speech Recognition (ASR) transcription accuracy remains highly sensitive to audio segmentation strategies, yet most benchmarks assume oracle timestamps unavailable in deployment. We systematically evaluate how audio segmentation affects Whisper’s performance on 10 hours of Persian YouTube content, comparing transcript-aligned (oracle) versus silence-based (realistic) approaches across contrasting acoustic conditions. Results reveal striking content-type dependency: podcast content benefits from timestamp segmentation (33% lower mean WER), while entertainment content favors silence-based segmentation (8% lower mean WER). This finding demonstrates that optimal segmentation must be content-aware, with silence detection better capturing natural boundaries in acoustically heterogeneous media while avoiding mid-utterance splits. We publicly release our evaluation framework, 10 hours of audio with gold transcripts, and segmentation results here: https://github.com/ri164-bolleit/persian-youtube-whisper-benchmark
%U https://aclanthology.org/2026.silkroadnlp-1.13/
%P 121-130

Markdown (Informal)

[Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content](https://aclanthology.org/2026.silkroadnlp-1.13/) (Iranmanesh et al., SilkRoadNLP 2026)

• Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content (Iranmanesh et al., SilkRoadNLP 2026)

ACL

• Reihaneh Iranmanesh, Rojin Ziaei, and Joe Garman. 2026. Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content. In The Proceedings of the First Workshop on NLP and LLMs for the Iranian Language Family, pages 121–130, Rabat, Morocco. Association for Computational Linguistics.