Influence of emotion and focus location on prosody in matched statements and questions
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
Preliminary data were collected on how emotional qualities of the voice (sad, happy, angry) influence the acoustic underpinnings of neutral sentences varying in location of intra-sentential focus (initial, final, no) and utterance "modality" (statement, question). Short (six syllable) and long (ten syllable) utterances exhibiting varying combinations of emotion, focus, and modality characteristics were analyzed for eight elderly speakers following administration of a controlled elicitation paradigm (story completion) and a speaker evaluation procedure. Duration and fundamental frequency (f0) parameters of recordings were scrutinized for "keyword" vowels within each token and for whole utterances. Results generally re-affirmed past accounts of how duration and f0 are encoded on key content words to mark linguistic focus in affectively neutral statements and questions for English. Acoustic data on three "global" parameters of the stimuli (speech rate, mean f0, f0 range) were also largely supportive of previous descriptions of how happy, sad, angry, and neutral utterances are differentiated in the speech signal. Important interactions between emotional and linguistic properties of the utterances emerged which were predominantly (although not exclusively) tied to the modulation of f0; speakers were notably constrained in conditions which required them to manipulate f0 parameters to express emotional and nonemotional intentions conjointly. Sentence length also had a meaningful impact on some of the measures gathered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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