# BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech

arXiv: https://arxiv.org/abs/2505.12192 · HTML: https://mlsanity.com/paper/2505.12192/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 90 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review](https://mlsanity.com/paper/2407.17844/) (2024)
- [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)
- [Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech](https://mlsanity.com/paper/2411.08013/) (2024)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method](https://mlsanity.com/paper/2210.01205/) (2022)
- [Comparative Study of Speech Analysis Methods to Predict Parkinson's Disease](https://mlsanity.com/paper/2111.10207/) (2021)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Multimodal Audio-based Disease Prediction with Transformer-based Hierarchical Fusion Network](https://mlsanity.com/paper/2410.09289/) (2024)

## Built on by (most cited first, 1 of 1)

- [Zero-Shot Parkinson's Disease Detection from Speech: Comparing Large Audio and Language Models](https://mlsanity.com/paper/2605.24806/) (2026)

## Abstract (arXiv)

Early detection of PD remains particularly challenging in resource-constrained settings, where voice-based analysis has emerged as a promising non-invasive and cost-effective alternative. However, existing studies predominantly focus on English or other major languages; notably, no voice dataset for PD exists for Bengali -- a language spoken by over 230 million people worldwide -- posing a significant barrier to culturally inclusive and accessible healthcare solutions. We present BenSparX, the first Bengali conversational speech dataset for PD detection, along with a robust and explainable ML framework tailored for early diagnosis. The proposed framework incorporates diverse acoustic feature categories, systematic feature selection methods, and state-of-the-art ML classifiers with extensive hyperparameter optimization. Furthermore, to enhance interpretability and trust in model predictions, the framework incorporates SHAP (SHapley Additive exPlanations) analysis to quantify the contribution of individual acoustic features toward PD detection. Our framework achieves state-of-the-art performance, yielding an accuracy of 95.67%, F1 score of 95.62%, and AUC of 0.990. We further validated our approach by applying the framework to existing PD datasets in other languages, where it consistently outperforms state-of-the-art approaches. This study lays the foundation for identifying subtle yet clinically meaningful vocal biomarkers, particularly in low-resource settings such as Bengali-speaking populations, and represents a significant step toward equitable, explainable, and robust digital health diagnostics for neurodegenerative disorders. The labelled acoustic-feature dataset derived from the audio recordings in this study is available at https://github.com/riadEDU/BenSParX.