Processing emotional prosody in a foreign language: the case of German and Hebrew
Bibliographic record
Abstract
This study investigated the universality of emotional prosody in perception of discrete emotions when semantics is not available. In two experiments the perception of emotional prosody in Hebrew and German by listeners who speak one of the languages but not the other was investigated. Having a parallel tool in both languages allowed to conduct controlled comparisons. In Experiment 1, 39 native German speakers with no knowledge of Hebrew and 80 native Israeli speakers rated Hebrew sentences spoken with four different emotional prosodies (anger, fear, happiness, sadness) or neutral. The Hebrew version of the Test for Rating of Emotions in Speech (T-RES) was used for this purpose. Ratings indicated participants' agreement on how much the sentence conveyed each of four discrete emotions (anger, fear, happiness and sadness). In Experient 2, 30 native speakers of German, and 24 Israeli native speakers of Hebrew who had no knowledge of German rated sentences of the German version of the T-RES. Based only on the prosody, German-speaking participants were able to accurately identify the emotions in the Hebrew sentences and Hebrew-speaking participants were able to identify the emotions in the German sentences. In both experiments ratings between the groups were similar. These findings show that individuals are able to identify emotions in a foreign language even if they do not have access to semantics. This ability goes beyond identification of target emotion; similarities between languages exist even for "wrong" perception. This adds to accumulating evidence in the literature on the universality of emotional prosody. Supplementary Information: The online version contains supplementary material available at 10.1007/s41809-022-00107-x.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.000 |
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".