Evidence map›Paper›PMID 42582879›Full record

ArticlePNAS nexus2026

Brief speech samples reveal emotional states in daily life.

Timo K Koch, Gabriella M Harari, Samuel D Gosling, Zachariah Marrero, Ramona Schoedel, Markus Bühner, Clemens Stachl

Abstract read
In one paragraph

Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Timo K KochInstitute of Behavioral Science and Technology, University of St Gallen, St Gallen 9000, Switzerland.ORCID https://orcid.org/0000-0001-6728-2063
Gabriella M HarariDepartment of Communication, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0001-9258-9075
Samuel D GoslingDepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA.ORCID https://orcid.org/0000-0001-8970-591X
Zachariah MarreroDepartment of Psychology, The University of Texas at Austin, Austin, TX 78712, USA.
Ramona SchoedelDepartment of Psychology, Ludwig-Maximilians-Universität München, Munich 80802, Germany.ORCID https://orcid.org/0000-0001-7275-0626
Markus BühnerDepartment of Psychology, Ludwig-Maximilians-Universität München, Munich 80802, Germany.ORCID https://orcid.org/0000-0002-0597-8708
Clemens StachlInstitute of Behavioral Science and Technology, University of St Gallen, St Gallen 9000, Switzerland.ORCID https://orcid.org/0000-0002-4498-3067

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Does our speech-"what" we say and "how" we say it-reveal how we subjectively feel in daily life? Speech and emotion are often thought to be linked, but most supporting evidence comes from controlled laboratory settings and focuses on expressed, enacted, or externally labeled emotions, leaving open the question of whether naturalistic speech reflects subjective emotional experience in daily life. To address this question, we extracted both modern foundation-model embeddings and established speech-analysis variables from brief, prompted smartphone recordings, including Linguistic Inquiry and Word Count features and prosodic descriptors, and used them as inputs to supervised machine learning models predicting concurrent self-reported emotional states (934 participants; 12,285 observations). Cross-validated models evaluated on unseen participants captured information about self-reported emotional states (contentment: median Spearman

Indexed as

emotional statesfoundation modelssmartphonesspeechvoice

Identifiers

PMID42582879
PMCPMC13458938

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.