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Record W1603157621

Effects of emotional content and emotional voice on speech intelligibility in younger and older adults

2008· article· en· W1603157621 on OpenAlexaffvenue
Kate Dupuis, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAudiologyPsychologyArousalIntelligibility (philosophy)Valence (chemistry)Emotional valenceCorrelationActive listeningCognitionCommunicationSocial psychologyMedicineMathematics
DOInot available

Abstract

fetched live from OpenAlex

The effects of emotional content and emotional voice on speech intelligibility in younger and older adults was investigated. Twenty-eight younger adults with good health and clinically normal hearing thresholds in the speech range were tested. The stimuli used were the 200 sentences from the NU6 lists. The stimuli were presented to one group visually as text on paper, and to two groups auditorally, through two loudspeakers in a sound-attenuating booth. Means were obtained for both valence and arousal ratings for all three groups. The SNR threshold data were collected on young adults with normal hearing by Richard Wilson and colleagues using the female voice. Analyses revealed a significant positive correlation between valence and arousal for participants in the visual condition. The results indicated that the emotional arousal of listeners to a particular word can affect intelligibility, depending on the modality of presentation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.243
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2008
Admission routes2
Has abstractyes

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