ASR-Style Transcript Normalization Removes Useful Pragmatic Signal for Dialogue Act Classification
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 transcript conditions across six classifiers (TF-IDF logistic regression, RoBERTa, BERT, ALBERT, GPT-2, and BiLSTM) and three conversational corpora (Switchboard, ICSI MRDA, and SLUE-HVB), supplemented by multi-seed experiments, normalization controls, and real-ASR evaluations. Across five random seeds, heavy normalization reduces Macro-F1 in all 15 neural model-corpus combinations under the original preprocessing protocols. Removal using the original filler an...
| Authors | Lucky Onyekwelu-Udoka, Md Shafiqul Islam |
|---|---|
| Primary category | cs.CL |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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