ArticleSensors (Basel, Switzerland)2024
Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 40 citations in OpenAlex.
- Machine Learning Approaches to Early Detection of Parkinson's Disease Using Speech Analysis Technique.Neurology international · 2026Article
- A novel multimodal AI framework for early diagnosis of idiopathic Parkinson's disease.Medical & biological engineering & computing · 2026Article
- Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026Review
- On the Suitability of Data Augmentation Techniques to Improve Parkinson's Disease Detection with Speech Recordings.Diagnostics (Basel, Switzerland) · 2026Article
- MIL-O-PD: a two-stage multiple instance learning and heuristic optimization framework for unpaired multimodal Parkinson's diagnosis.Frontiers in aging neuroscience · 2026Article
- Effects of Ketogenic Diet on Quality of Life in Parkinson Disease: An Integrative Review.Nutrients · 2025Review
- An enhanced deep learning approach for speaker diarization using TitaNet, MarbelNet and time delay network.Scientific reports · 2025Article
- Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson's Disease Detection.Journal of imaging · 2025Article
- Analysis of Voice, Speech, and Language Biomarkers of Parkinson's Disease Collected in a Mixed Reality Setting.Sensors (Basel, Switzerland) · 2025Article
- Ranking pre-trained speech embeddings in Parkinson's disease detection: Does Wav2Vec 2.0 outperform its 1.0 version across speech modes and languages?Computational and structural biotechnology journal · 2025Article
- A review of machine learning and deep learning for Parkinson's disease detection.Discover artificial intelligence · 2025Review
- Unveiling early signs of Parkinson's disease via a longitudinal analysis of celebrity speech recordings.NPJ Parkinson's disease · 2024Article
- Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.Sensors (Basel, Switzerland) · 2024Article
- A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.