Prosodic and Semantic Effects on the Perception of Mixed Emotions in Speech
Bibliographic record
Abstract
The current study examines the perception of mixed happy-sad emotions elicited by a combination of prosodic voice cues (pitch and tempo), and sentence content (semantics) in speech. In the first experiment, participants will rate sentences spoken by a female talker on happiness and sadness using a 7-point Likert scale. In the second experiment, the processing of emotions will be examined using eye-tracking. Participants will watch audio-visual recordings of a female talker speaking a series of sentences and will rate the emotional expressions using the same rating scale. When pitch and tempo cues are consistent with happy and sad expressions, we expect listeners to rate the expressions in accordance with these emotions. However, when voice cues that signal happy and sad emotions are in conflict, they will result in intermediate happiness and sadness ratings, reflecting the perception of mixed happy-sad emotions. We expect that eye-tracking measures will reveal shorter durations of looking time to purely happy or sad emotions in comparison to mixed happy-sad emotions. Furthermore, the semantics of sentence content will reduce the perception of mixed happy-sad emotions evoked in vocal expressions. The findings from the current study are expected to extend our knowledge on the perception of mixed emotions in normal populations and in special populations with social-emotional deficits. Discipline: Psychology Honours Faculty Mentor: Dr. Tara Vongpaisal
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".