Effects of acoustic cue manipulations on emotional prosody recognition.
Bibliographic record
Abstract
Studies on emotion recognition from prosody have largely focused on the role and effectiveness of isolated acoustic parameters and less is known about how information from these cues is perceived and combined to infer emotional meaning. To better understand how acoustic cues influence recognition of discrete emotions from voice, this study investigated how listeners perceptually combine information from two critical acoustic cues, pitch and speech rate, to identify emotions. For all the utterances, pitch and speech rate measures of the whole utterance were independently manipulated by factors of 1.25 (+25%) and 0.75 (−25%). To examine the influence of one cue with reference to the other cue the three manipulations of pitch (+25%, 0%, and −25%) were crossed with the three manipulations of speech rate (+25%, 0%, and −25%). Pseudoutterances spoken in five emotional tones (happy, sad, angry, fear, and disgust) and neutral that have undergone acoustic cue manipulations were presented to 15 male and 15 female participants for an emotion identification task. Results indicated that both pitch and speech rate are important acoustic parameters to identify emotions and more critically, it is the relative weight of each cue which seems to contribute significantly for categorizing happy, sad, fear, and neutral.
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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 | 0.001 |
| 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".