# Identification of primary and collateral tracks in stuttered speech

arXiv: https://arxiv.org/abs/2003.01018 · HTML: https://mlsanity.com/paper/2003.01018/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [Analysis and Tuning of a Voice Assistant System for Dysfluent Speech](https://mlsanity.com/paper/2106.11759/) (2021)
- [Automatic Disfluency Detection from Untranscribed Speech](https://mlsanity.com/paper/2311.00867/) (2023)
- [Clinical Annotations for Automatic Stuttering Severity Assessment](https://mlsanity.com/paper/2506.00644/) (2025)

## Abstract (arXiv)

Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective aiming at modeling these events and detect them for downstream tasks. In addition, previous works often used different metrics depending on whether the input features are text or speech, making it difficult to compare the different contributions. Here, we introduce a new evaluation framework for disfluency detection inspired by the clinical and NLP perspective together with the theory of performance from citeclark1996using which distinguishes between primary and collateral tracks. We introduce a novel forced-aligned disfluency dataset from a corpus of semi-directed interviews, and present baseline results directly comparing the performance of text-based features (word and span information) and speech-based (acoustic-prosodic information). Finally, we introduce new audio features inspired by the word-based span features. We show experimentally that using these features outperformed the baselines for speech-based predictions on the present dataset.