The neural representation of emotional cues investigated using the speech frequency following response: A potential tool to evaluate speech prosody
Bibliographic record
Abstract
Background: The Speech-evoked Frequency Following Response (sFFR) provides spctro-temporal data on speech processing in the auditory system. Its effectiveness in extracting prosodic features like variations in fundamental frequency (F0 contour) and intensity is uncertain. Objectives: This study examines how well sFFR tracks F0 contour in different emotions using a natural two-syllable word. It also explores talker’s gender impact on F0 contours and gender disparity in encoding prosodic cues. Method: The word “balloon” spoken by male and female speakers with sad and happy emotions, elicited FFR from 16 adults (8 males, aged 18–31). A pitch estimation algorithm calculated root mean squared error and 5% accuracy to evaluate the response’s fidelity to F0 contour under different conditions. Results: The sFFR tracked prosodic speech features, influenced by emotion type and talker voice characteristics. Participants identified emotions most accurately from sad male voices. Lower F0 trajectories corresponded to more reliable FFR responses, showing better tracking of male voices and sad emotions. No significant gender-related differences were observed in emotional data processing. Conclusion: These findings highlight sFFR’s utility in capturing dynamic speech properties and its potential in clinical assessments. Future research should explore prosody processing in hearing-impaired individuals and consider integrating sFFR into diagnostic protocols.
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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 | 0.000 | 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) | 0.002 | 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".