Emotional prosody recognition using pseudowords from the Hoosier Vocal Emotions Collection
Bibliographic record
Abstract
ABSTRACT Purpose: to verify whether the Hoosier Vocal Emotions Collection corpus allows the identification of different emotional prosodies in Brazilian adults. Methods: 60 healthy adults equally distributed by sex, aged between 18 and 42 years, participated in the Mini-Mental State Examination and subtests related to prosody (Montreal communication battery and those from the Hoosier Vocal Emotions Collection corpus, with 73 pseudowords produced by two different actresses). The results were analyzed using descriptive statistics and the Chi-square test, which had a significance of 5%. Results: in general, the emotional prosodies from the Hoosier Vocal Emotions Collection were identified with an average accuracy of 43.63%, with the highest hits, in descending order, for neutrality, sadness, happiness, disgust, anger, and fear. As for sex, there were statistically significant differences regarding the correct answers in the neutrality and disgust prosodies for males, while for females, there were differences in happiness and anger prosodies. Both sexes had more incredible difficulty in identifying prosody related to fear. Conclusion: the Hoosier Vocal Emotions Collection corpus allowed the identification of the emotional prosodies tested in the studied sample, with sexual dysmorphism to emotional prosodic identification being found.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 0.002 |
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".