# Impact of Speech Mode in Automatic Pathological Speech Detection

arXiv: https://arxiv.org/abs/2406.09968 · HTML: https://mlsanity.com/paper/2406.09968/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [Machine Learning for Stuttering Identification: Review, Challenges and Future Directions](https://mlsanity.com/paper/2107.04057/) (2021)
- [Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech](https://mlsanity.com/paper/2309.14107/) (2023)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detection](https://mlsanity.com/paper/2110.03283/) (2021)
- [Supervised Speech Representation Learning for Parkinson's Disease Classification](https://mlsanity.com/paper/2106.00531/) (2021)
- [End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge](https://mlsanity.com/paper/2207.10817/) (2022)
- [Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning](https://mlsanity.com/paper/2302.11343/) (2023)
- [Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings](https://mlsanity.com/paper/2306.00689/) (2023)

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

- [Graph Neural Networks for Parkinsons Disease Detection](https://mlsanity.com/paper/2409.07884/) (2024)
- [Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection](https://mlsanity.com/paper/2409.17276/) (2024)

## Abstract (arXiv)

Automatic pathological speech detection approaches yield promising results in identifying various pathologies. These approaches are typically designed and evaluated for phonetically-controlled speech scenarios, where speakers are prompted to articulate identical phonetic content. While gathering controlled speech recordings can be laborious, spontaneous speech can be conveniently acquired as potential patients navigate their daily routines. Further, spontaneous speech can be valuable in detecting subtle and abstract cues of pathological speech. Nonetheless, the efficacy of automatic pathological speech detection for spontaneous speech remains unexplored. This paper analyzes the influence of speech mode on pathological speech detection approaches, examining two distinct categories of approaches, i.e., classical machine learning and deep learning. Results indicate that classical approaches may struggle to capture pathology-discriminant cues in spontaneous speech. In contrast, deep learning approaches demonstrate superior performance, managing to extract additional cues that were previously inaccessible in non-spontaneous speech