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Parkinson Disease l: Introduction01:24

Parkinson Disease l: Introduction

Parkinson’s disease is a chronic, progressive neurodegenerative disorder that primarily affects movement. It is characterized by motor symptoms such as resting tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. Patients may notice hand tremors at rest, stiffness during movement, or a shuffling gait. In addition to motor features, non-motor symptoms include sleep disturbances, mood and behavioral changes, constipation, and cognitive impairment, all of which...

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Related Experiment Video

Updated: Jul 18, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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Speech-Based Parkinson's Detection Using Pre-Trained Self-Supervised Automatic Speech Recognition (ASR) Models and

Hadi Sedigh Malekroodi1, Nuwan Madusanka2, Byeong-Il Lee1,2,3

  • 1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Bioengineering (Basel, Switzerland)
|July 29, 2025
PubMed
Summary

This study shows that advanced speech analysis using Automatic Speech Recognition (ASR) models can effectively detect Parkinson's disease (PD). Fine-tuned ASR models with contrastive learning significantly outperformed traditional methods for early PD diagnosis.

Keywords:
HuBERTParkinson’s disease (PD)Wav2Vec 2.0deep learningsupervised contrastive learningtransfer learning

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Area of Science:

  • Neurology
  • Computational Linguistics
  • Machine Learning

Background:

  • Speech impairments are early indicators of Parkinson's disease (PD).
  • Traditional methods for PD diagnosis can be invasive or lack sensitivity for early detection.
  • Automatic Speech Recognition (ASR) models offer potential for non-invasive PD assessment.

Purpose of the Study:

  • To evaluate the efficacy of fine-tuned pre-trained ASR models (Wav2Vec 2.0, HuBERT) for Parkinson's disease detection.
  • To enhance PD detection by integrating supervised contrastive (SupCon) learning with ASR models.
  • To compare ASR-derived features against established acoustic features (MFCCs, eGeMAPS) and assess model interpretability using Grad-CAM.

Main Methods:

  • Fine-tuning Wav2Vec 2.0 and HuBERT models on the NeuroVoz dataset for PD detection.
  • Implementing supervised contrastive (SupCon) learning to improve feature discrimination.
  • Comparing ASR features with mel-frequency cepstral coefficients (MFCCs) and eGeMAPS.
  • Utilizing Grad-CAM for visualizing speech regions critical for PD prediction.

Main Results:

  • Pre-trained ASR models significantly outperformed baseline acoustic features in PD detection.
  • The SupCon learning approach consistently yielded better results than traditional cross-entropy (CE) models.
  • Wav2Vec 2.0 and HuBERT with SupCon achieved high F1 scores (90.0% and 88.99%) and superior AUC values compared to CE models.

Conclusions:

  • Fine-tuned ASR models, particularly Wav2Vec 2.0 and HuBERT with SupCon, demonstrate high accuracy for non-invasive PD detection.
  • ASR-based speech analysis presents a scalable and promising tool for early diagnosis and monitoring of Parkinson's disease.
  • The integration of advanced machine learning techniques enhances the potential of speech analysis in neurological disorder assessment.