ArticleNPJ Parkinson's disease2024
Unveiling early signs of Parkinson's disease via a longitudinal analysis of celebrity speech recordings.
Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Pilot Study of VGGish-CNN as a Model for the Classification of Parkinson's Disease from a Control Group Using Speech Impairment, Integrated with Explainable Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2026Article
- The Relationship Between Voluntary Rapid Blinking Rate and Future Development of Neurodegenerative Disease.Geriatrics & gerontology international · 2026Article
- Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.Scientific reports · 2026Article
- Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment.NPJ digital medicine · 2026Article
- Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.Movement disorders clinical practice · 2026Review
- 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
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Numerous studies proposed methods to detect Parkinson's disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who publicly disclosed their PD diagnosis provide longitudinal data, allowing the creation of a new corpus, ParkCeleb. We collected videos from 40 subjects with PD and 40 controls and analyzed evolving speech features from 10 years before to 20 years after diagnosis. Our longitudinal analysis, focused on 15 subjects with PD and 15 controls, revealed features like pitch variability, pause duration, speech rate, and syllable duration, indicating PD progression. Early dysarthria patterns were detectable in the prodromal phase, with the best classifiers achieving AUCs of 0.72 and 0.75 for data collected ten and five years before diagnosis, respectively, and 0.93 post-diagnosis. This study highlights the potential for early detection methods, aiding treatment response identification and screening in clinical trials.
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Registered trials
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