# Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech

arXiv: https://arxiv.org/abs/2503.10301 · HTML: https://mlsanity.com/paper/2503.10301/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain](https://mlsanity.com/paper/2203.17004/) (2022)
- [MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra](https://mlsanity.com/paper/2305.13686/) (2023)
- [rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method](https://mlsanity.com/paper/1906.03588/) (2019)
- [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)
- [Domain Adversarial Neural Networks for Dysarthric Speech Recognition](https://mlsanity.com/paper/2010.03623/) (2020)
- [Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization](https://mlsanity.com/paper/2106.10127/) (2021)

## Built on by (most cited first, 7 of 7)

- [Exploring Generative Error Correction for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.20163/) (2025)
- [MVP: Multi-source Voice Pathology detection](https://mlsanity.com/paper/2505.20050/) (2025)
- [From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease](https://mlsanity.com/paper/2507.16836/) (2025)
- [A Benchmark for Early-stage Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2605.14066/) (2026)
- [Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention](https://mlsanity.com/paper/2606.09271/) (2026)
- [Motor, Cognitive, or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection](https://mlsanity.com/paper/2608.13425/) (2026)
- [Cross-Lingual Parkinson's Disease Severity Assessment Using Pre-trained Speech Embeddings: A Multi-Class Evaluation](https://mlsanity.com/paper/2609.20875/) (2026)

## Abstract (arXiv)

This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for type-based binary classification. One head is specialized for diadochokinetic patterns. The other head looks for natural speech patterns present in continuous spoken utterances. Only one of the two heads is operative accordingly to the nature of the input. Speech representations are extracted from self-supervised learning (SSL) models and wavelet transforms. Adaptive layers, convolutional bottlenecks, and contrastive learning are exploited to reduce variations across languages. Our solution is assessed against two distinct datasets, EWA-DB, and PC-GITA, which cover Slovak and Spanish languages, respectively. Results indicate that conventional models trained on a single language dataset struggle with cross-linguistic generalization, and naive combinations of datasets are suboptimal. In contrast, our model improves generalization on both languages, simultaneously.