SynthesisFrontiers in digital health2022
Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.
Synthesis in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Unmet needs in functional neurological disorder related to digital healthcare.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Acoustic voice analysis of women with migraine.Arquivos de neuro-psiquiatria · 2026Article
- Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.Scientific reports · 2026Article
- TENG-Based Self-Powered Silent Speech Recognition Interface: from Assistive Communication to Immersive AR/VR Interaction.Nano-micro letters · 2026Article
- Development and validation of a machine learning model to detect psychiatric symptoms in Huntington's disease using speech analysis.PloS one · 2026Article
- Voice disorders classification using machine learning: a scoping review.Frontiers in digital health · 2026Review
- Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).Frontiers in digital health · 2026Article
- Outside the healthy bounds: reference intervals for population and individual-level medical speech analysis.Frontiers in digital health · 2026Article
- Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection.Journal of healthcare informatics research · 2025Article
- The relationship between cognitive impairment and acoustic features in major depressive disorder: a longitudinal cohort study.BMC psychiatry · 2025Article
- Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025Article
- Pitfalls in using ML to predict cognitive function performance.Scientific reports · 2025Article
- From Screening to Precision: Searching for Voice Disorder-Specific Acoustic and Auditory-Perceptual Metrics.Journal of voice : official journal of the Voice Foundation · 2025Article
- Collection and Analysis of Repeated Speech Samples: Methodological Framework and Example Protocol.JMIR research protocols · 2025Article
- Analysis of Voice, Speech, and Language Biomarkers of Parkinson's Disease Collected in a Mixed Reality Setting.Sensors (Basel, Switzerland) · 2025Article
- Developing digital health technologies for frontotemporal degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Linguistic changes in spontaneous speech for detecting Parkinson's disease using large language models.PLOS digital health · 2025Article
- Acoustic-based machine learning approaches for depression detection in Chinese university students.Frontiers in public health · 2025Article
- Associations of Vocal Features, Psychiatric Symptoms, and Cognitive Functions in Schizophrenia.Neuropsychiatric disease and treatment · 2025Article
- Predicting affective engagement and mental strain from prosodic speech features.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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