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    <abstract>Web video makes massive spoken-language corpora possible, but automatic speech recognition (ASR) routinely normalizes away fillers, repetitions, repairs, and hesitations. Because Conversation Analysis (CA) transcripts meticulously document these interactional phenomena, they offer an ideal yet underutilized resource for ASR adaptation. We show that fine-tuning Whisper large-v3 on just 4.47 hours of CA-annotated Australian and US conversation dramatically improves verbatim transcription on heterogeneous web speech without sacrificing lexical accuracy. On a 57-clip benchmark from 39 Australian and New Zealand local-government channels, fine-tuning reduces verbatim word error rate from 16.06% to 11.39% (a 29% relative reduction) and raises filler recovery F1 from .117 to .819. These gains transfer directly to New Zealand speech absent from training (reducing verbatim error from 18.24% to 11.71%), demonstrating that small CA datasets teach the model a generalizable transcription policy rather than narrow acoustic memorization. We argue that targeted verbatim adaptation should become standard practice in web-as-corpus pipelines to ensure large-scale resources like CoANZSE preserve the interactional evidence needed for linguistic research.</abstract>
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%0 Conference Proceedings
%T Keeping the Ums: Conversation-Analysis-Informed Verbatim ASR for Web Speech
%A Coats, Steven
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F coats-2026-keeping
%X Web video makes massive spoken-language corpora possible, but automatic speech recognition (ASR) routinely normalizes away fillers, repetitions, repairs, and hesitations. Because Conversation Analysis (CA) transcripts meticulously document these interactional phenomena, they offer an ideal yet underutilized resource for ASR adaptation. We show that fine-tuning Whisper large-v3 on just 4.47 hours of CA-annotated Australian and US conversation dramatically improves verbatim transcription on heterogeneous web speech without sacrificing lexical accuracy. On a 57-clip benchmark from 39 Australian and New Zealand local-government channels, fine-tuning reduces verbatim word error rate from 16.06% to 11.39% (a 29% relative reduction) and raises filler recovery F1 from .117 to .819. These gains transfer directly to New Zealand speech absent from training (reducing verbatim error from 18.24% to 11.71%), demonstrating that small CA datasets teach the model a generalizable transcription policy rather than narrow acoustic memorization. We argue that targeted verbatim adaptation should become standard practice in web-as-corpus pipelines to ensure large-scale resources like CoANZSE preserve the interactional evidence needed for linguistic research.
%U https://aclanthology.org/2026.wac-1.18/
%P 192-199

Markdown (Informal)

[Keeping the Ums: Conversation-Analysis-Informed Verbatim ASR for Web Speech](https://aclanthology.org/2026.wac-1.18/) (Coats, WAC 2026)

• Keeping the Ums: Conversation-Analysis-Informed Verbatim ASR for Web Speech (Coats, WAC 2026)

ACL

• Steven Coats. 2026. Keeping the Ums: Conversation-Analysis-Informed Verbatim ASR for Web Speech. In Proceedings of the 13th Web-as-Corpus Workshop, pages 192–199, Budapest, Hungary. Association for Computational Linguistics.