ArticlePloS one2024
Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech and Language Markers as Longitudinal Predictors of Youth Mental Health: A Systematic Review.Early intervention in psychiatry · 2025Pooled it
- Technology matters: AI-driven tools in children's mental healthcare: perspectives from young people and parents.Child and adolescent mental health · 2026Article
- Decoding Social Cues: A Scoping Review of AI-Supported Emotional Competencies and Their Impact on School-Aged Populations.Journal of Intelligence · 2026Review
- Maternal expressions of warmth and negativity and adolescent mental health: using longitudinal monozygotic twin-difference analyses to approach causal inference.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Correction: Automatic detection of expressed emotion from five-minute speech samples: Challenges and opportunities.PloS one · 2025Article
- Automatic detection of expressed emotion from Five-Minute Speech Samples: Challenges and opportunities.PloS one · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events. Manual coding of these events requires time-consuming specialist training, amplifying the need for automated approaches. Herein we describe an automated machine learning approach for determining the degree of warmth, a key component of EE, from acoustic and text natural language features. Our dataset of 52 recorded interviews is taken from recordings, collected over 20 years ago, from a nationally representative birth cohort of British twin children, and was manually coded for EE by two researchers (inter-rater reliability 0.84-0.90). We demonstrate that the degree of warmth can be predicted with an F1-score of 64.7% despite working with audio recordings of highly variable quality. Our highly promising results suggest that machine learning may be able to assist in the coding of EE in the near future.
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Registered trials
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.