# Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review

arXiv: https://arxiv.org/abs/2407.17844 · HTML: https://mlsanity.com/paper/2407.17844/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 87 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention](https://mlsanity.com/paper/2402.04563/) (2024)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [NeuroVoz: a Castillian Spanish corpus of parkinsonian speech](https://mlsanity.com/paper/2403.02371/) (2024)
- [Listenable Maps for Audio Classifiers](https://mlsanity.com/paper/2403.13086/) (2024)
- [Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages](https://mlsanity.com/paper/2305.11284/) (2023)
- [X-vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2007.03599/) (2020)
- [Supervised Speech Representation Learning for Parkinson's Disease Classification](https://mlsanity.com/paper/2106.00531/) (2021)
- [Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages](https://mlsanity.com/paper/2002.04374/) (2020)
- [Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease](https://mlsanity.com/paper/2111.10207/) (2021)
- [Parkinson's disease diagnostics using AI and natural language knowledge transfer](https://mlsanity.com/paper/2204.12559/) (2022)

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

- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Evaluating the Usefulness of Non-Diagnostic Speech Data for Developing Parkinson's Disease Classifiers](https://mlsanity.com/paper/2505.18722/) (2025)
- [Evaluating the Effectiveness of Pre-Trained Audio Embeddings for Classification of Parkinson's Disease Speech Data](https://mlsanity.com/paper/2506.02078/) (2025)
- [BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech](https://mlsanity.com/paper/2505.12192/) (2025)
- [RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification](https://mlsanity.com/paper/2507.03594/) (2025)
- [A Benchmark for Early-stage Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2605.14066/) (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)

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches.