Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends
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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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Methods to collect and analyze natural speech could be the basis of speech-language digital assessments that are low-burden, non-invasive, highly reproducible, and widely accessible.
“For example, methods to collect and analyze natural speech, a highly complex clinical behavior that can track pathological features in an objective and quantifiable way, 18 could be the basis of speech‐language digital assessments that are low‐burden, non‐invasive, highly reproducible, and widely accessible.”
From Developing digital health technologies for frontotemporal degeneration (2025) · cites this paper as [18]
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- Authors
- Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B.
- Affiliations
- Hasso Plattner Institute (DE)
- University of Augsburg (DE)
- Corresponding author
- Hecker, Pascal
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2022-07-07
- Volume
- 4
- Pages
- 842301-842301
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC9309252
- OpenAlex
- W4284679137
- Cited by
- 78
- References
- 99
- Citations by year
- 2026: 27 · 2025: 26 · 2024: 11 · 2023: 12 · 2022: 1
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- publish.UP (University of Potsdam) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Universität Potsdam (cc-by)
- Funders
- Universität Potsdam
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- voice analysis, neurological disorders, Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis, depression, bipolar disorder, stress, aphasia, dysarthria, dysphonia, speech disorders, acoustic features, speech dataset, long-term monitoring, mental illness, neurodegenerative diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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