Bibliographic record
Abstract
Preliminary data were gathered on how simulated emotional characteristics of the voice influence the acoustic form of English utterances containing specific combinations of intonational and stress features [extending Eady and Cooper, J. Acoust. Soc. Am. 80, 402–415 (1986)]. Utterances varying in intrasentential focus position (initial, final, none) were elicited as both statements and questions in each of four emotional ‘‘modes’’ (neutral, sad, happy, angry) employing a structured elicitation procedure. Parameters of duration and fundamental frequency (f0) were then determined for the productions elicited from eight elderly speakers to specify important acoustic dimensions associated with specific combinations of stress, ‘‘modality,’’ and emotional features of the stimuli. Results of the acoustic analyses largely reaffirmed past accounts of how contrastive focus is encoded in (affectively neutral) statements and questions for English, and cohered well with the acoustic literature on how basic emotions are expressed vocally for three key acoustic parameters (mean f0, f0 range, speech rate). The impact of emotion on linguistic attributes of prosodic structure was most evident in the speakers’ modulation of f0, which was notably constrained in prosodic conditions where speakers were required to signal ‘‘marked’’ emotional and nonemotional intentions conjointly within the intonation contour. [Work funded by FCAR.]
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".