Prosody and Semantics Are Separate but Not Separable Channels in the Perception of Emotional Speech: Test for Rating of Emotions in Speech
Bibliographic record
Abstract
PURPOSE: Our aim is to explore the complex interplay of prosody (tone of speech) and semantics (verbal content) in the perception of discrete emotions in speech. METHOD: We implement a novel tool, the Test for Rating of Emotions in Speech. Eighty native English speakers were presented with spoken sentences made of different combinations of 5 discrete emotions (anger, fear, happiness, sadness, and neutral) presented in prosody and semantics. Listeners were asked to rate the sentence as a whole, integrating both speech channels, or to focus on one channel only (prosody or semantics). RESULTS: We observed supremacy of congruency, failure of selective attention, and prosodic dominance. Supremacy of congruency means that a sentence that presents the same emotion in both speech channels was rated highest; failure of selective attention means that listeners were unable to selectively attend to one channel when instructed; and prosodic dominance means that prosodic information plays a larger role than semantics in processing emotional speech. CONCLUSIONS: Emotional prosody and semantics are separate but not separable channels, and it is difficult to perceive one without the influence of the other. Our findings indicate that the Test for Rating of Emotions in Speech can reveal specific aspects in the processing of emotional speech and may in the future prove useful for understanding emotion-processing deficits in individuals with pathologies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".