Speech and Voice Biomarkers for Parkinson’s disease Detection: A Systematic Review of Machine Learning and Deep Learning Models
Abstract
Abstract Parkinson's disease (PD) is a progressive disease that impacts movement and other functions. Voice disorders can present early in the disease process and voice analysis is an appealing non-invasive means of detection and monitoring of PD. The aim of the present review is to explore the application of machine learning (ML) and deep learning (DL) techniques to the diagnosis and prognosis of PD using voice signals. We applied the PRISMA 2020 guidelines to search ScienceDirect, IEEE Xplore, and ACM Digital Library for studies from the last eight years (2017–2025) with specific set criteria. A large number of public datasets, such as UCI Parkinson's data set and PC-GITA corpus, are included in this review. It briefly reviews some of the important acoustic characteristics, including jitter, shimmer, harmonic-to-noise ratio (HNR), mel-frequency cepstral coefficients (MFCCs), nonlinear dynamic measures, spectrogram features, and wavelet features. The review also covers the selection of features, reduction of data, and optimization of models. It provides a comparison of traditional ML models such as Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), Logistic Regression with advanced DL models including Convolutional Neural Networks (CNNs), recurrent neural networks, transformers and hybrid models that incorporate handcrafted and learned features. Last but not least, it shows how explainable artificial intelligence (XAI) can facilitate easier…
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Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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