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Mapping 74 Years in Acoustic Analysis of Voice Disorders: A Bibliometric Review and Future Research Directions
Journal article   Peer reviewed

Mapping 74 Years in Acoustic Analysis of Voice Disorders: A Bibliometric Review and Future Research Directions

Ahmed M. Yousef, Lady Catherine Cantor-Cutiva and Eric J. Hunter
Journal of communication disorders, Vol.117, 106555
09/2025
DOI: 10.1016/j.jcomdis.2025.106555
PMCID: PMC12357485
PMID: 40712472
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC12357485/View
Open Access

Abstract

•Bibliometric analysis covers acoustic voice assessment research from 1951 to 2024.•Machine learning is a growing area in voice disorder acoustic assessments.•Dementia and ALS are underassessed compared to Parkinson’s in the literature.•Four themes: physiology, dysphonia, treatments, and machine learning.•Study reveals gaps and future directions for acoustic voice assessment research. This paper conducts a bibliometric analysis to identify and examine the strengths, gaps, and trends in research on acoustic voice assessment for voice disorders. A bibliometric analysis was performed on journal articles about voice disorders and acoustic voice assessment in English, Spanish, and Portuguese using seven indexed databases. The analyzed bibliometric parameters included publication year, authors, institutions, countries, journals, subject areas, and keywords. VOSviewer software was used for keyword co-occurrence analysis and authorships network analysis. The initial search yielded 6,532 publications, with 1,253 relevant papers after screening (1951-2024). Publications in acoustic voice assessment had 74 years of exponential growth (25% published after 2021). The publishing journals covered 80 categories/subjects; Artificial Intelligence, though recent, was among the top journal subjects. Health conditions like dementia, Alzheimer’s, Amyotrophic lateral sclerosis, and depression were underassessed compared to Parkinson’s. The literature focused on four separate themes: physiology of voice-affecting conditions; speech acoustics for evaluating dysphonia; speech production measurements for treating voice disorders; machine learning integration for voice disorder assessment. Taking a wide view of acoustic voice assessment demonstrated research strengths and gaps—highlighting where it is used and not used—and the co-occurrence of various voice assessment topics. These insights reveal future opportunities to implement acoustic voice assessment.
Acoustics Bibliometrics Digital Health Literature Review Speech Acoustics Voice Assessment Voice Disorders

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