A Cross-Linguistic Validation of the Test for Rating Emotions in Speech: Acoustic Analyses of Emotional Sentences in English, German, and Hebrew
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: The Test for Rating Emotions in Speech (T-RES) has been developed in order to assess the processing of emotions in spoken language. In this tool, spoken sentences, which are composed of emotional content (anger, happiness, sadness, and neutral) in both semantics and prosody in different combinations, are rated by listeners. To date, English, German, and Hebrew versions have been developed, as well as online versions, iT-RES, to adapt to COVID-19 social restrictions. Since the perception of spoken emotions may be affected by linguistic (and cultural) variables, it is important to compare the acoustic characteristics of the stimuli within and between languages. The goal of the current report was to provide cross-linguistic acoustic validation of the T-RES. METHOD: T-RES sentences in the aforementioned languages were acoustically analyzed in terms of mean F0, F0 range, and speech rate to obtain profiles of acoustic parameters for different emotions. RESULTS: Significant within-language discriminability of prosodic emotions was found, for both mean F0 and speech rate. Similarly, these measures were associated with comparable patterns of prosodic emotions for each of the tested languages and emotional ratings. CONCLUSIONS: The results demonstrate the lack of dependence of prosody and semantics within the T-RES stimuli. These findings illustrate the listeners' ability to clearly distinguish between the different prosodic emotions in each language, providing a cross-linguistic validation of the T-RES and iT-RES.
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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 | 0.003 | 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 | 0.000 | 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) | 0.000 | 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 it