Emotional prosody recognition using pseudowords from the Hoosier Vocal Emotions Collection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it