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ASR transcript normalization harms dialogue act classification, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores how the common practice of normalizing transcripts from Automatic Speech Recognition (ASR) systems can inadvertently remove crucial pragmatic signals needed for dialogue act classification. The study evaluated nine different transcript conditions across six classifiers and three conversational corpora, finding that heavy normalization consistently degraded performance for neural models. Specifically, removing filled pauses and punctuation negatively impacted classification accuracy, demonstrating that such normalization should be task-dependent rather than applied indiscriminately. AI

IMPACT Highlights the need for careful consideration of transcript preprocessing in ASR pipelines to preserve pragmatic signals for downstream NLP tasks.

RANK_REASON Research paper published on arXiv detailing findings about ASR transcript normalization. [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 →

ASR transcript normalization harms dialogue act classification, study finds

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Research paper published on arXiv detailing findings about ASR transcript normalization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lucky Onyekwelu-Udoka, Md Shafiqul Islam ·

    ASR-Style Transcript Normalization Removes Useful Pragmatic Signal for Dialogue Act Classification

    arXiv:2610.22934v1 Announce Type: new Abstract: Automatic speech recognition (ASR) pipelines often normalize transcripts by removing filled pauses and punctuation and expanding colloquial forms, potentially discarding cues useful for dialogue act classification. We evaluate nine …

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