# Cross-Lingual Parkinson's Disease Severity Assessment Using Pre-trained Speech Embeddings: A Multi-Class Evaluation

arXiv: https://arxiv.org/abs/2609.20875 · HTML: https://mlsanity.com/paper/2609.20875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 16 Sep 2026.

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

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions](https://mlsanity.com/paper/2406.16128/) (2024)
- [Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis](https://mlsanity.com/paper/2412.02006/) (2024)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2503.10301/) (2025)
- [Speech as a Biomarker for Disease Detection](https://mlsanity.com/paper/2409.10230/) (2024)
- [Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease](https://mlsanity.com/paper/2603.22225/) (2026)
- [Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features](https://mlsanity.com/paper/2308.09042/) (2023)
- [Evaluating the Effectiveness of Pre-Trained Audio Embeddings for Classification of Parkinson's Disease Speech Data](https://mlsanity.com/paper/2506.02078/) (2025)

## Abstract (arXiv)

Parkinson's disease (PD) often manifests through speech impairments, facilitating accessible, non-invasive, and cost-effective severity assessment for early diagnosis and progression tracking. Despite advances in speech foundation models (SFMs), their cross-lingual generalization for PD severity multi-class classification remains underexplored due to limited labeled data, a lack of explainable methods and variability across languages and datasets. In this work, we evaluate pre-trained embeddings from four state-of-the-art open-source SFMs across three datasets in zero-shot and k-shot cross-lingual settings for multi-class PD severity assessment. Our results show that pre-trained speech embeddings enable meaningful cross-lingual transfer, although performance is sensitive to dataset properties, preprocessing, and adaptation strategy. Misclassifications under these conditions related to inter-speaker variability and atypical speech patterns highlight the need for more robust feature extraction and modeling for PD severity assessment while emphasizing the importance of explainability for reliable clinical insights.