Bibliographic record
Abstract
A study was initiated to acoustically characterize and differentiate discrete categories of negatively valenced emotions conveyed through speech prosody. Utterances elicited from eight encoders (actors) in different emotional tones were perceptually rated by a group of decoders to gauge how strongly each token was associated with the basic emotions of ‘‘anger,’’ ‘‘disgust,’’ and ‘‘sadness’’ using a seven-choice response paradigm. Tokens rated as highly representative of each target emotion by greater than 80% of decoders were examined acoustically. Measures of fundamental frequency (mean, range, sd), amplitude (mean, range, sd), and duration (speech rate, %voiced) were obtained from each token and for utterances spoken in a ‘‘neutral’’ tone by the same encoders. Normalized measures were compared among emotional categories to uncover reliable acoustic dimensions that may have contributed to perceptually distinct vocal symbols of negative emotion states. Results pointed to important differences in duration, amplitude, and especially fundamental frequency in discriminating among prosodic signals representing distinct negative emotions. These findings extend work on the acoustic underpinnings of positive and negative vocalizations in speech [M. D. Pell, J. Acoust. Soc. Am. 109, 1668–1680 (2001)], providing finer specification of these parameters within the family of ‘‘negative’’ emotions. [Work supported by NSERC.]
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".