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: 3 complete abstracts (557 words), 9 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 process whereby an utterance is decoded into a representation in terms of linguistic units (sequences of phonetic segments which combine to form lexical and grammatical morphemes).
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: Perception, Speech; Speech Discrimination; Discrimination, Speech.
Where it fits in the MeSH classification
- Broader topics
- Auditory Perception
- See also
- Speech Recognition Software; Voice Recognition
Research landscape
PubMed indexes 32,628 records tagged with the MeSH heading “Speech Perception” (counts retrieved 2026-10-31). 8,278 of them (25.4%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 10,723 records.
Reviews account for 1,537 records (4.7%) and meta-analyses for 154. Randomized controlled trials number 676 (2.1%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 6,819 |
| 2000-2009 | 6,808 |
| 2010-2019 | 10,723 |
| 2020 onward | 8,278 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 1,537 | 4.7% |
| Meta-analyses | 154 | 0.5% |
| Randomized controlled trials | 676 | 2.1% |
Method: PubMed query "Speech Perception"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in psychiatry and psychology
Research on mental health and behaviour relies on questionnaires, interviews and rating scales rather than physical measurements, so how a condition was defined and measured shapes every result. Many studies are observational or small, placebo responses can be large, and findings in one group or culture may not carry over to another. Nothing on this page is advice about diagnosis or treatment; anyone worried about their mental health should talk to a qualified clinician. Check how outcomes were measured, who took part and how long they were followed.
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 |
|---|---|---|---|---|
| Stimulus dependencies-rather than next-word prediction-can explain pre-onset brain encoding in naturalistic listening designs | 2026 | Research article Design not recorded in structured metadata | 11 | cc by PMID 41960890 · DOI · PMC13068430 |
| Optimized feature gains explain and predict successes and failures of human selective listening | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 41826525 · DOI · PMC13192276 |
| Encoding of speech modes and loudness in ventral precentral gyrus | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 41986329 · DOI · PMC13270037 |
Stimulus dependencies-rather than next-word prediction-can explain pre-onset brain encoding in naturalistic listening designs [1]
Abstract reading summary — source excerpts
- Opening excerpt
- The human brain is thought to constantly predict future words during language processing.
- Closing excerpt
- This questions the extent to which this new encoding-based method can be used to study prediction in the brain.
- 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 (221 words)
The human brain is thought to constantly predict future words during language processing. Recently, a new approach emerged that aims to capture neural prediction directly by using vector representations of words (embeddings) to predict brain activity prior to word onset. Two findings have been proposed as hallmarks of neural next-word prediction: (i) significant encoding prior to word onset and (ii) its modulation by word predictability. However, natural language is rife with temporal correlations, where upcoming words share statistical information with preceding ones. This raises a critical question: Do these hallmarks emerge from the brain actively predicting future content, or might they be equally well explained by the regression model exploiting these inherent stimulus dependencies? To distinguish between these alternatives, we applied the same encoding analysis to passive control systems, i.e., representational systems that encode the stimulus but cannot predict upcoming words. We show that both hallmarks emerge in two such control systems, namely in word embeddings themselves and in speech acoustics. We further show that proposed methods to correct for these dependencies are insufficient, as the effects persist even after such corrections. Together, these results suggest that pre-onset prediction of brain activity might reflect dependencies in natural language rather than predictive computations. This questions the extent to which this new encoding-based method can be used to study prediction in the brain.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41960890 · DOI · PMC13068430
Optimized feature gains explain and predict successes and failures of human selective listening [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Attention facilitates communication by enabling selective listening to sound sources of interest.
- Closing excerpt
- The results suggest that human-like attentional strategies naturally arise from the optimization of feature gains for selective listening.
- 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 (153 words)
Attention facilitates communication by enabling selective listening to sound sources of interest. However, little is known about why attentional selection succeeds in some conditions but fails in others. While neurophysiology implicates multiplicative feature gains in selective attention, it is unclear whether such gains can explain real-world attention-driven behaviour. Here we optimized an artificial neural network with stimulus-computable feature gains to recognize a cued talker's speech from binaural audio in 'cocktail party' scenarios. Though not trained to mimic humans, the model produced human-like performance across diverse real-world conditions, exhibiting selection based both on voice qualities and on spatial location as well as selection failures in conditions where humans tended to fail. It also predicted novel attentional effects that we confirmed in human experiments, and exhibited signatures of 'late selection' like those seen in human auditory cortex. The results suggest that human-like attentional strategies naturally arise from the optimization of feature gains for selective listening.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41826525 · DOI · PMC13192276
Encoding of speech modes and loudness in ventral precentral gyrus [3]
Abstract reading summary — source excerpts
- Opening excerpt
- The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication.
- Closing excerpt
- These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.
- 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 (183 words)
The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with 94% and 89% accuracy for the two participants, and corresponding neural preparatory activity at 640 ms and 270 ms before speech onset enabled 80% decoding accuracy, respectively. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41986329 · DOI · PMC13270037
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 Listening Effort: How the Cognitive Consequences of Acoustic Challenge Are Reflected in Brain and Behavior [4]
- Research article Phonological skills and their role in learning to read: a meta-analytic review [5]
- Research article Hallucinations and Strong Priors [6]
- Research article Neural Entrainment and Attentional Selection in the Listening Brain [7]
- Research article Speech rhythms and their neural foundations [8]
- Research article The Encoding of Speech Sounds in the Superior Temporal Gyrus [9]
References
- Schönmann I, Szewczyk J, de Lange FP, Heilbron M. Stimulus dependencies-rather than next-word prediction-can explain pre-onset brain encoding in naturalistic listening designs. . 2026. PMID 41960890 · DOI · PMC13068430
- Griffith IM, Hess RP, McDermott JH. Optimized feature gains explain and predict successes and failures of human selective listening. . 2026. PMID 41826525 · DOI · PMC13192276
- Srinivasan A, Wairagkar M, Iacobacci C, Hou X, Card NS, Jacques BG, et al.. Encoding of speech modes and loudness in ventral precentral gyrus. . 2026. PMID 41986329 · DOI · PMC13270037
- Peelle JE. Listening Effort: How the Cognitive Consequences of Acoustic Challenge Are Reflected in Brain and Behavior. Ear Hear. 2018. PMID 28938250 · DOI · PMC5821557
- Melby-Lervåg M, Lyster SA, Hulme C. Phonological skills and their role in learning to read: a meta-analytic review. Psychol Bull. 2012. PMID 22250824 · DOI
- Corlett PR, Horga G, Fletcher PC, Alderson-Day B, Schmack K, Powers AR. Hallucinations and Strong Priors. Trends Cogn Sci. 2019. PMID 30583945 · DOI · PMC6368358
- Obleser J, Kayser C. Neural Entrainment and Attentional Selection in the Listening Brain. Trends Cogn Sci. 2019. PMID 31606386 · DOI
- Poeppel D, Assaneo MF. Speech rhythms and their neural foundations. Nat Rev Neurosci. 2020. PMID 32376899 · DOI
- Yi HG, Leonard MK, Chang EF. The Encoding of Speech Sounds in the Superior Temporal Gyrus. Neuron. 2019. PMID 31220442 · DOI · PMC6602075
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 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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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.