Rapid neural analysis of linguistic stress and meter in continuous speech

preprint OA: closed CC-BY-NC-ND-4.0

Abstract

Continuous speech evolves around vowels, the centerpieces of individual syllables. Vowels vary in linguistic and acoustic salience: Linguistically, stressed syllables are more salient than unstressed syllables: Stress patterns convey critical lexico-semantic and prosodic information, and their regularity defines the speech meter. Acoustically, English vowel intensity cues lexical stress but also marks salient syllables irrespective of stress status. Recent evidence demonstrates rapid neural analysis of vowel intensity and identity during perception of continuous speech. Here, we probe how these processes integrate lexical stress and metrical regularity. We recorded EEG while participants (n=26) listened to children’s stories with either an irregular, speech-like meter, or a regular poetic meter. Stress and meter modulated cortical encoding of vowels throughout processing: Preparatory activity preceded vowel onsets in an irregular meter only, and early sensory responses were enhanced for unstressed vowels, suggesting additional resource allocation during processing of uncertain and less discriminable speech sounds. In contrast, later processing (300-500ms) was stronger for stressed syllables and in irregular meters, suggesting a combined effect of uncertainty and informational content. Finally, responses were stronger for small intensity rises within metrically predicted stressed vowels than in all other conditions. In the time-frequency domain, the spectral profile of neural phase-locking corresponded to spectral signatures of individual evoked responses, syllable and stress rates in the stimuli. Overall, our findings reveal rapid neural integration of stress and metrical expectations in neural processing of continuous speech. These dynamics may underlie the perceptual benefits of metrically regular speech, such as poetry and song lyrics.
Full text 1,992 characters · extracted from oa-doi-fallback · click to expand
Abstract Continuous speech evolves around vowels, the centerpieces of individual syllables. Vowels vary in linguistic and acoustic salience: Linguistically, stressed syllables are more salient than unstressed syllables: Stress patterns convey critical lexico-semantic and prosodic information, and their regularity defines the speech meter. Acoustically, English vowel intensity cues lexical stress but also marks salient syllables irrespective of stress status. Recent evidence demonstrates rapid neural analysis of vowel intensity and identity during perception of continuous speech. Here, we probe how these processes integrate lexical stress and metrical regularity. We recorded EEG while participants (n=26) listened to children’s stories with either an irregular, speech-like meter, or a regular poetic meter. Stress and meter modulated cortical encoding of vowels throughout processing: Preparatory activity preceded vowel onsets in an irregular meter only, and early sensory responses were enhanced for unstressed vowels, suggesting additional resource allocation during processing of uncertain and less discriminable speech sounds. In contrast, later processing (300-500ms) was stronger for stressed syllables and in irregular meters, suggesting a combined effect of uncertainty and informational content. Finally, responses were stronger for small intensity rises within metrically predicted stressed vowels than in all other conditions. In the time-frequency domain, the spectral profile of neural phase-locking corresponded to spectral signatures of individual evoked responses, syllable and stress rates in the stimuli. Overall, our findings reveal rapid neural integration of stress and metrical expectations in neural processing of continuous speech. These dynamics may underlie the perceptual benefits of metrically regular speech, such as poetry and song lyrics. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵+ co-senior authorship.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-28T02:00:01.590549+00:00
License: CC-BY-NC-ND-4.0