Lexical effects in the production of emotional speech
Bibliographic record
Abstract
Emotion in speech production has been shown to correlate well with fundamental frequency (F0), intensity, and duration [B. Zupan etal., J.Commun. Disord., 42, 1–17 (2009)]. Studies in non-emotional speech have shown effects of lexical predictors on speech production (e.g., neighborhood density [B. Munson and N. P. Solomon. J. Speech, Lang. Hear. Res., 47, 1048–1058 (2004)], and lexical frequency [R. H. Baayen etal., in Proceedings of the Annual Meeting of the Chicago Linguistic Society (2007), Vol. 43, pp. 1–29]. In the present study, we investigate the effects of lexical predictors in the production of emotional speech. Two professional male actors recorded 260 isolated words. Three emotional states were analyzed: neutral, anger, and joy. Measures of word frequency, morphological family size, number of synonyms and homophones, and ratings for danger and usefulness [L. Wurm, Psychon. Bull. Rev., 14, 1218–1225 (2007)] were used as statistical predictors in modeling, using linear mixed-effects regression. Following previous literature, both speakers use F0, intensity, and duration to portray emotion. Lexical predictors such as word frequency, morphological family size, and danger ratings were found to significantly predict mean F0, mean intensity, and word duration across emotional types for both speakers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".