# Supervised Speech Representation Learning for Parkinson's Disease Classification

arXiv: https://arxiv.org/abs/2106.00531 · HTML: https://mlsanity.com/paper/2106.00531/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Impact of Speech Mode in Automatic Pathological Speech Detection](https://mlsanity.com/paper/2406.09968/) (2024)
- [Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection](https://mlsanity.com/paper/2409.17276/) (2024)
- [Selfsupervised learning for pathological speech detection](https://mlsanity.com/paper/2406.02572/) (2024)
- [Investigating the Effects of Diffusion-based Conditional Generative Speech Models Used for Speech Enhancement on Dysarthric Speech](https://mlsanity.com/paper/2412.13933/) (2024)
- [Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention](https://mlsanity.com/paper/2606.09271/) (2026)

## Abstract (arXiv)

Recently proposed automatic pathological speech classification techniques use unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since these representations are learned based on reconstructing the input, there is no guarantee that they are robust to pathology-unrelated cues such as speaker identity information. Further, these representations are not necessarily discriminative for pathology detection. In this paper, we exploit supervised auto-encoders to extract robust and discriminative speech representations for Parkinson's disease classification. To reduce the influence of speaker variabilities unrelated to pathology, we propose to obtain speaker identity-invariant representations by adversarial training of an auto-encoder and a speaker identification task. To obtain a discriminative representation, we propose to jointly train an auto-encoder and a pathological speech classifier. Experimental results on a Spanish database show that the proposed supervised representation learning methods yield more robust and discriminative representations for automatically classifying Parkinson's disease speech, outperforming the baseline unsupervised representation learning system.