Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (689 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The sounds produced by humans by the passage of air through the larynx and over the vocal cords, and then modified by the resonance organs, the nasopharynx, and the mouth.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Voices.
Where it fits in the MeSH classification
- Broader topics
- Respiratory Physiological Phenomena
- Narrower topics
- Voice Quality
- See also
- Phonation; Speech
Related concepts
Research landscape
PubMed indexes 17,760 records tagged with the MeSH heading “Voice” (counts retrieved 2026-10-31). 4,369 of them (24.6%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 5,713 records.
Reviews account for 1,052 records (5.9%) and meta-analyses for 124. Randomized controlled trials number 466 (2.6%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 4,813 |
| 2000-2009 | 2,865 |
| 2010-2019 | 5,713 |
| 2020 onward | 4,369 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 1,052 | 5.9% |
| Meta-analyses | 124 | 0.7% |
| Randomized controlled trials | 466 | 2.6% |
Method: PubMed query "Voice"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in anatomy and physiology
Anatomy describes how body structures are built and physiology describes how they normally work, so most findings here are descriptive or mechanistic rather than evidence about treatment. Much of what is known comes from dissection, imaging, tissue studies and experiments in animals, and measurements in healthy volunteers can differ from those in people with disease or at different ages. Normal ranges vary between individuals and populations. Check whether a result was measured in people or in another species, the age and health of the participants, and the method used.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system | 2024 | Research article Design not recorded in structured metadata | 71 | cc by PMID 38472193 · DOI · PMC10933441 |
| A machine learning method to process voice samples for identification of Parkinson's disease | 2023 | Research article Design not recorded in structured metadata | 36 | cc by PMID 37996478 · DOI · PMC10667335 |
| The time course of person perception from voices in the brain | 2024 | Research article Design not recorded in structured metadata | 28 | cc by PMID 38889147 · DOI · PMC11214051 |
| Speak or shout? Nonverbal vocalizations promote rapid detection of emotions in vocal communication | 2026 | Research article Design not recorded in structured metadata | 3 | cc by PMID 41505457 · DOI · PMC12782396 |
Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia.
- Closing excerpt
- We expect this approach could facilitate the restoration of normal voice function and significantly enhance the quality of life for patients with dysfunctional vocal folds.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (152 words)
Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia. Here, we present a self-powered wearable sensing-actuation system based on soft magnetoelasticity that enables assisted speaking without relying on the vocal folds. It holds a lightweighted mass of approximately 7.2 g, skin-alike modulus of 7.83 × 105 Pa, stability against skin perspiration, and a maximum stretchability of 164%. The wearable sensing component can effectively capture extrinsic laryngeal muscle movement and convert them into high-fidelity and analyzable electrical signals, which can be translated into speech signals with the assistance of machine learning algorithms with an accuracy of 94.68%. Then, with the wearable actuation component, the speech could be expressed as voice signals while circumventing vocal fold vibration. We expect this approach could facilitate the restoration of normal voice function and significantly enhance the quality of life for patients with dysfunctional vocal folds.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38472193 · DOI · PMC10933441
A machine learning method to process voice samples for identification of Parkinson's disease [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data.
- Closing excerpt
- We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (176 words)
Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 37996478 · DOI · PMC10667335
The time course of person perception from voices in the brain [3]
Abstract reading summary — source excerpts
- Opening excerpt
- When listeners hear a voice, they rapidly form a complex first impression of who the person behind that voice might be.
- Closing excerpt
- The findings align with recent theoretical models and shed light on the computations underpinning person perception from voices.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (159 words)
When listeners hear a voice, they rapidly form a complex first impression of who the person behind that voice might be. We characterize how these multivariate first impressions from voices emerge over time across different levels of abstraction using electroencephalography and representational similarity analysis. We find that for eight perceived physical (gender, age, and health), trait (attractiveness, dominance, and trustworthiness), and social characteristics (educatedness and professionalism), representations emerge early (~80 ms after stimulus onset), with voice acoustics contributing to those representations between ~100 ms and 400 ms. While impressions of person characteristics are highly correlated, we can find evidence for highly abstracted, independent representations of individual person characteristics. These abstracted representationse merge gradually over time. That is, representations of physical characteristics (age, gender) arise early (from ~120 ms), while representations of some trait and social characteristics emerge later (~360 ms onward). The findings align with recent theoretical models and shed light on the computations underpinning person perception from voices.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38889147 · DOI · PMC11214051
Speak or shout? Nonverbal vocalizations promote rapid detection of emotions in vocal communication [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Human vocal expressions of emotion can be expressed nonverbally, through vocalizations such as shouts or laughter, or speakers can embed emotional meanings in language by modifying their tone of voice ("prosody").
- Closing excerpt
- Our data show that vocalizations are functionally suited to build robust, rapid impressions of a speaker's emotion state unconstrained by the listener's linguistic cultural background.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (202 words)
Human vocal expressions of emotion can be expressed nonverbally, through vocalizations such as shouts or laughter, or speakers can embed emotional meanings in language by modifying their tone of voice ("prosody"). Is there evidence that nonverbal expressions promote "better" (i.e., more accurate, faster) recognition of emotions than speech, and what is the impact of language experience? Our study investigated these questions using a cross-cultural gating paradigm, in which Chinese and Arab listeners (n = 25/group) judged the emotion communicated by acoustic events that varied in duration (200 milliseconds to the full expression) and form (vocalizations or prosody expressed in listeners' native, second or foreign language). Accuracy was higher for vocalizations overall, but listeners were markedly more efficient to form stable categorical representations of the speaker's emotion from vocalizations (M = 417ms) than native prosody (M = 765ms). Language experience enhanced recognition of emotional prosody expressed by native/ingroup speakers for some listeners (Chinese) but not all (Arab), emphasizing the dynamic interplay of socio-cultural factors and stimulus quality on prosody recognition which occurs over a more sustained time window. Our data show that vocalizations are functionally suited to build robust, rapid impressions of a speaker's emotion state unconstrained by the listener's linguistic cultural background.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41505457 · DOI · PMC12782396
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Recommended Protocols for Instrumental Assessment of Voice: American Speech-Language-Hearing Association Expert Panel to Develop a Protocol for Instrumental Assessment of Vocal Function [5]
- Research article Pathophysiology and treatment of muscle tension dysphonia: a review of the current knowledge [6]
- Research article Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence [7]
- Research article Understanding voice perception [8]
- Research article Face and Voice Perception: Understanding Commonalities and Differences [9]
- Research article Voice disorders in teachers. A review [10]
References
- Che Z, Wan X, Xu J, Duan C, Zheng T, Chen J. Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. . 2024. PMID 38472193 · DOI · PMC10933441
- Iyer A, Kemp A, Rahmatallah Y, Pillai L, Glover A, Prior F, et al.. A machine learning method to process voice samples for identification of Parkinson's disease. . 2023. PMID 37996478 · DOI · PMC10667335
- Lavan N, Rinke P, Scharinger M. The time course of person perception from voices in the brain. . 2024. PMID 38889147 · DOI · PMC11214051
- Pell MD, Cui H, Mori Y, Jiang X. Speak or shout? Nonverbal vocalizations promote rapid detection of emotions in vocal communication. . 2026. PMID 41505457 · DOI · PMC12782396
- Patel RR, Awan SN, Barkmeier-Kraemer J, Courey M, Deliyski D, Eadie T, et al.. Recommended Protocols for Instrumental Assessment of Voice: American Speech-Language-Hearing Association Expert Panel to Develop a Protocol for Instrumental Assessment of Vocal Function. Am J Speech Lang Pathol. 2018. PMID 29955816 · DOI
- Van Houtte E, Van Lierde K, Claeys S. Pathophysiology and treatment of muscle tension dysphonia: a review of the current knowledge. J Voice. 2011. PMID 20400263 · DOI
- Schirmer A, Adolphs R. Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence. Trends Cogn Sci. 2017. PMID 28173998 · DOI · PMC5334135
- Belin P, Bestelmeyer PE, Latinus M, Watson R. Understanding voice perception. Br J Psychol. 2011. PMID 21988380 · DOI
- Young AW, Frühholz S, Schweinberger SR. Face and Voice Perception: Understanding Commonalities and Differences. Trends Cogn Sci. 2020. PMID 32298625 · DOI
- Martins RH, Pereira ER, Hidalgo CB, Tavares EL. Voice disorders in teachers. A review. J Voice. 2014. PMID 24929935 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-31.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.