# Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages

arXiv: https://arxiv.org/abs/2002.04374 · HTML: https://mlsanity.com/paper/2002.04374/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease](https://mlsanity.com/paper/2603.22225/) (2026)
- [Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers](https://mlsanity.com/paper/2505.18722/) (2025)
- [BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech](https://mlsanity.com/paper/2505.12192/) (2025)
- [Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice](https://mlsanity.com/paper/2508.20717/) (2025)

## Abstract (arXiv)

Parkinson's disease patients develop different speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows the development of computer aided tools to support the diagnosis and the evaluation of the disease severity. This paper introduces a methodology to classify Parkinson's disease from speech in three different languages: Spanish, German, and Czech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy among the three languages. The transfer learning scheme aims to improve the accuracy of the models when the weights of the neural network are initialized with utterances from a different language than the used for the test set. The results suggest that the proposed strategy improves the accuracy of the models in up to 8% when the base model used to initialize the weights of the classifier is robust enough. In addition, the results obtained after the transfer learning are in most cases more balanced in terms of specificity-sensitivity than those trained without the transfer learning strategy.