# A Comprehensive Rubric for Annotating Pathological Speech

arXiv: https://arxiv.org/abs/2404.18851 · HTML: https://mlsanity.com/paper/2404.18851/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0](https://mlsanity.com/paper/2204.03417/) (2022)
- [KSoF: The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering](https://mlsanity.com/paper/2203.05383/) (2022)
- [Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification](https://mlsanity.com/paper/2305.18392/) (2023)

## Abstract (arXiv)

Rubrics are a commonly used tool for labeling voice corpora in speech quality assessment, although their application in the context of pathological speech remains relatively limited. In this study, we introduce a comprehensive rubric based on various dimensions of speech quality, including phonetics, fluency, and prosody. The objective is to establish standardized criteria for identifying errors within the speech of individuals with Down syndrome, thereby enabling the development of automated assessment systems. To achieve this objective, we utilized the Prautocal corpus. To assess the quality of annotations using our rubric, two experiments were conducted, focusing on phonetics and fluency. For phonetic evaluation, we employed the Goodness of Pronunciation (GoP) metric, utilizing automatic segmentation systems and correlating the results with evaluations conducted by a specialized speech therapist. While the obtained correlation values were not notably high, a positive trend was observed. In terms of fluency assessment, deep learning models like wav2vec were used to extract audio features, and we employed an SVM classifier trained on a corpus focused on identifying fluency issues to categorize Prautocal corpus samples. The outcomes highlight the complexities of evaluating such phenomena, with variability depending on the specific type of disfluency detected.