The influence of pitch and speech rate on emotional prosody recognition: psychological and neuro-cognitive perspectives
Bibliographic record
Abstract
Speech prosody is an essential aspect of human communication and vital to determine the emotional state of the speaker. Multiple acoustic cues are harnessed to infer emotional meaning from prosody, from which pitch and speech rate have been shown to be extremely important and reliable predictors to make emotion judgments. A comprehensive understanding of the underlying mechanisms involved in decoding pitch and speech rate cues is still underway. To have a complete understanding of the processing of prosodic cues, the current thesis addressed this issue from multiple levels â psychological and neuro-cognitive. The first experiment investigated how listeners combine information from the two critical cues â pitch and speech rate â for emotion recognition. The two cues were systematically manipulated in a factorial manner in pseudo-language-like utterances spoken in different emotional tones. The findings of this experiment demonstrated that pitch and speech rate are important parameters for accurate emotion recognition; however, the relative weight of the two cues is distinct for each emotion. Therefore, listeners harness the two cues differently for each emotion and it varies depending on the acoustic properties of each emotion. The second experiment explored the role of the two hemispheres in the brain in decoding pitch and speech rate to make emotion inferences. To this end, the ability of individuals with lesions to either the right or left hemisphere was compared to age-matched healthy participants in evaluating emotion information from the same two cues. Greater difficulty experienced by the right-hemisphere-damaged patients provides evidence for right hemisphere specialization in extracting acoustic properties for emotion recognition. In a third experiment, tasks from Experiment 2 were presented to healthy adults in an fMRI paradigm to identify the specific neural structures engaged in processing pitch and speech rate cues. The results reinforce that extracting pitch cues to make emotion inferences requires greater contribution from the right superior temporal gyrus/sulcus (STG/STS); however, the data shows processing speech rate cues involves both right and left STG/STS region. In summary, the present thesis provides important information about the complex processing involved at psychological and neurocognitive levels in decoding physical properties of the speech for emotion recognition.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".