Emotional Speech Processing at the Intersection of Prosody and Semantics
Bibliographic record
Abstract
The ability to accurately perceive emotions is crucial for effective social interaction. Many questions remain regarding how different sources of emotional cues in speech (e.g., prosody, semantic information) are processed during emotional communication. Using a cross-modal emotional priming paradigm (Facial affect decision task), we compared the relative contributions of processing utterances with single-channel (prosody-only) versus multi-channel (prosody and semantic) cues on the perception of happy, sad, and angry emotional expressions. Our data show that emotional speech cues produce robust congruency effects on decisions about an emotionally related face target, although no processing advantage occurred when prime stimuli contained multi-channel as opposed to single-channel speech cues. Our data suggest that utterances with prosodic cues alone and utterances with combined prosody and semantic cues both activate knowledge that leads to emotional congruency (priming) effects, but that the convergence of these two information sources does not always heighten access to this knowledge during emotional speech processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".