# Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection

arXiv: https://arxiv.org/abs/1904.04388 · HTML: https://mlsanity.com/paper/1904.04388/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)
- [Filler Word Detection and Classification: A Dataset and Benchmark](https://mlsanity.com/paper/2203.15135/) (2022)
- [Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2312.00100/) (2023)
- [Streaming Joint Speech Recognition and Disfluency Detection](https://mlsanity.com/paper/2211.08726/) (2022)
- [Automatic Disfluency Detection from Untranscribed Speech](https://mlsanity.com/paper/2311.00867/) (2023)
- [Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives](https://mlsanity.com/paper/2301.10761/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [A novel multimodal dynamic fusion network for disfluency detection in spoken utterances](https://mlsanity.com/paper/2211.14700/) (2022)
- [Prosodic segmentation for parsing spoken dialogue](https://mlsanity.com/paper/2105.12667/) (2021)
- [End-to-End Speech Recognition and Disfluency Removal with Acoustic Language Model Pretraining](https://mlsanity.com/paper/2309.04516/) (2023)
- [An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification](https://mlsanity.com/paper/2510.11537/) (2025)

## Abstract (arXiv)

Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.