Stephanie Birkelbach

Disfluencies -- such as 'um,' 'uh,' interjections, parentheticals, and edited statements -- remain a persistent challenge for speech-driven systems, degrading accuracy in command interpretation, summarization, and conversational agents. We introduce DRES (Disfluency Removal Evaluation Suite), a controlled text-level benchmark that establishes a reproducible semantic upper bound for this task. DRES builds on human-annotated Switchboard transcripts, isolating disfluency removal from ASR errors and acoustic variability. We systematically evaluate proprietary and open-source LLMs across scales, prompting strategies, and architectures. Our results reveal that (i) simple segmentation consistently improves performance, even for long-context models; (ii) reasoning-oriented models tend to over-delete fluent tokens; and (iii) fine-tuning achieves near state-of-the-art precision and recall but harms generalization abilities. We further present a set of LLM-specific error modes and offer nine practical recommendations (R1-R9) for deploying disfluency removal in speech-driven pipelines. DRES provides a reproducible, model-agnostic foundation for advancing robust spoken-language systems.

@inproceedings{teleki25_dres,
title = {DRES: Benchmarking LLMs for Disfluency Removal},
author = {Maria Teleki and Sai Janjur and Haoran Liu and Oliver Grabner and Ketan Verma and Thomas Docog and Xiangjue Dong and Lingfeng Shi and Cong Wang and Stephanie Birkelbach and Jason Kim and Yin Zhang and James Caverlee},
year = {2025},
booktitle = {arXiv},
}