ArticleScientific reports2019
Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 59 citations in OpenAlex.
- Longitudinal Evaluation of Dysarthria Progression in Patients with Parkinson's Disease.Diagnostics (Basel, Switzerland) · 2026Article
- The Utility of Speech and Language Analytics for Screening Alzheimer's Disease.Neuro-degenerative diseases · 2026Review
- Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson's Disease Detection.Journal of imaging · 2025Article
- Vocal Feature Changes for Monitoring Parkinson's Disease Progression-A Systematic Review.Brain sciences · 2025Review
- NeuroVoz: a Castillian Spanish corpus of parkinsonian speech.Scientific data · 2024Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.Frontiers in aging neuroscience · 2024Article
- Robust and language-independent acoustic features in Parkinson's disease.Frontiers in neurology · 2023Article
- Article
- Article
- Speech phoneme and spectral smearing based non-invasive COVID-19 detection.Frontiers in artificial intelligence · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Literature documents the impact of Parkinson's Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identification of the disease. This study presents new approaches that are evaluated in three different corpora containing speakers suffering from PD with two main objectives: to investigate the influence of the different phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifiers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the differences in accuracy depending on the manner of articulation. Cross-validation results reach accuracies between 85% and 94% with AUC ranging from 0.91 to 0.98, while cross-corpora trials yield accuracies between 75% and 82% with AUC between 0.84 and 0.95, depending on the corpus. This is the first work analyzing the generalization properties of the proposed approaches employing cross-corpora trials and reaching high accuracies. Among the different phonemic groups, results suggest that plosives, vowels and fricatives are the most relevant acoustic segments for the detection of PD with the proposed schemes. In addition, the use of text-dependent utterances leads to more consistent and accurate models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.