Importance of F0 for predicting vocal emotion categorization
Bibliographic record
Abstract
Affective prosody is used to produce and express specific emotions to conversation partners. While pitch has been identified as a crucial cue for differentiating between emotions, there has been significant variation in the stimuli used by different groups of researchers to examine the acoustic cues necessary for the perception of vocal emotions. The Toronto Emotional Speech Set consists of 2800 items: 200 sentences (carrier phrase “say the word” followed by a target word) spoken by two adult female actors (one younger and one older) to portray seven emotions (anger, disgust, fear, sadness, happiness, pleasant surprise, neutral). In the current study, these stimuli were analyzed to determine which acoustical cues accounted for the most variance in categorizing stimuli into one of the seven pre-determined emotional conditions. The acoustical characteristics of mean duration, mean intensity, mean F0, mean range of intensity, and mean range of F0 were analyzed using a customized Praat script for each of the 2800 stimuli. For both talkers, mean F0 was the most important acoustical cue for accurately categorizing the TESS stimuli into the different emotional conditions. The second most important cue was F0 range for the younger talker and duration for the older talker.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".