ArticleBioengineering (Basel, Switzerland)2023
CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.JMIR medical informatics · 2026Article
- On the Suitability of Data Augmentation Techniques to Improve Parkinson's Disease Detection with Speech Recordings.Diagnostics (Basel, Switzerland) · 2026Article
- A novel class-attention transformer-driven feature fusion technique-based speech disorder classification.Frontiers in medicine · 2026Article
- Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.Biomedical engineering letters · 2025Article
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- Synchronous Analysis of Speech Production and Lips Movement to Detect Parkinson's Disease Using Deep Learning Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Pre-trained Convolutional Neural Networks Identify Parkinson's Disease from Spectrogram Images of Voice Samples.Research square · 2024Article
- In Silico Decoding of Parkinson's: Speech & Writing Analysis.Journal of clinical medicine · 2024Article
- Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.Bioengineering (Basel, Switzerland) · 2024Article
- Advanced AI techniques for classifying Alzheimer's disease and mild cognitive impairment.Frontiers in aging neuroscience · 2024Article
- Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.
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