# Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem

arXiv: https://arxiv.org/abs/2210.15982 · HTML: https://mlsanity.com/paper/2210.15982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

## Built on by (most cited first, 2 of 2)

- [Boli: A dataset for understanding stuttering experience and analyzing stuttered speech](https://mlsanity.com/paper/2501.15877/) (2025)
- [Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment](https://mlsanity.com/paper/2406.05784/) (2024)

## Abstract (arXiv)

Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or viewed detecting each dysfluency type as an isolated task; that does not capture the nature of stuttering, where one dysfluency seldom comes alone, i.e., co-occurs with others. This work explores an approach based on a modified wav2vec 2.0 system for end-to-end stuttering detection and classification as a multi-label problem. The method is evaluated on combinations of three datasets containing English and German stuttered speech, yielding state-of-the-art results for stuttering detection on the SEP-28k-Extended dataset. Experimental results provide evidence for the transferability of features and the generalizability of the method across datasets and languages.