ArticlePloS one2017
Towards the identification of Idiopathic Parkinson's Disease from the speech. New articulatory kinetic biomarkers.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A computer-aided diagnosis system of parkinson's disease based on hilbert spectrum features of speech.Scientific reports · 2025Article
- Analysis of Voice, Speech, and Language Biomarkers of Parkinson's Disease Collected in a Mixed Reality Setting.Sensors (Basel, Switzerland) · 2025Article
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Cross-device and test-retest reliability of speech acoustic measurements derived from consumer-grade mobile recording devices.Behavior research methods · 2024Article
- Pre-trained Convolutional Neural Networks Identify Parkinson's Disease from Spectrogram Images of Voice Samples.Research square · 2024Article
- NeuroVoz: a Castillian Spanish corpus of parkinsonian speech.Scientific data · 2024Article
- Unveiling early signs of Parkinson's disease via a longitudinal analysis of celebrity speech recordings.NPJ Parkinson's disease · 2024Article
- Exploring Spectrogram-Based Audio Classification for Parkinson's Disease: A Study on Speech Classification and Qualitative Reliability Verification.Sensors (Basel, Switzerland) · 2024Article
- Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.Frontiers in aging neuroscience · 2024Article
- Analyzing changes in parkinsonian speech over time: a diachronic experimental phonetics study.Frontiers in aging neuroscience · 2024Article
- A machine learning method to process voice samples for identification of Parkinson's disease.Scientific reports · 2023Article
- Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease.Heliyon · 2023Article
- Phonemes based detection of parkinson's disease for telehealth applications.Scientific reports · 2022Article
- A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.PloS one · 2022Article
- Assessing Parkinson's Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson's Voice Initiative.Diagnostics (Basel, Switzerland) · 2021Article
- (Dys)Prosody in Parkinson's Disease: Effects of Medication and Disease Duration on Intonation and Prosodic Phrasing.Brain sciences · 2021Article
- Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network.IEEE access : practical innovations, open solutions · 2021Article
- A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson's Disease Hypokinetic Dysarthria.Frontiers in human neuroscience · 2021Article
- Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease.Biosensors · 2019Article
- Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease.Scientific reports · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although a large amount of acoustic indicators have already been proposed in the literature to evaluate the hypokinetic dysarthria of people with Parkinson's Disease, the goal of this work is to identify and interpret new reliable and complementary articulatory biomarkers that could be applied to predict/evaluate Parkinson's Disease from a diadochokinetic test, contributing to the possibility of a further multidimensional analysis of the speech of parkinsonian patients. The new biomarkers proposed are based on the kinetic behaviour of the envelope trace, which is directly linked with the articulatory dysfunctions introduced by the disease since the early stages. The interest of these new articulatory indicators stands on their easiness of identification and interpretation, and their potential to be translated into computer based automatic methods to screen the disease from the speech. Throughout this paper, the accuracy provided by these acoustic kinetic biomarkers is compared with the one obtained with a baseline system based on speaker identification techniques. Results show accuracies around 85% that are in line with those obtained with the complex state of the art speaker recognition techniques, but with an easier physical interpretation, which open the possibility to be transferred to a clinical setting.
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