Exploring auditory emotion perception in cochlear implant users: Investigating the interplay of speech processing and affective signals
Bibliographic record
Abstract
Human emotions are intricately expressed through vocal sounds, encompassing affective prosody in speech and non-verbal cues such as screams and laughter. Recent evidence indicates that vocalizations take neurophysiological precedence over speech-embedded emotions and are generally easier to identify. However, Cochlear implant (CI) users still face challenges deciphering the subtle nuances in these primal signals. The implant's limited fidelity in transmitting acoustic information results in highly variable levels of emotion perception abilities among its users. Identifying the factors explaining this significant variability in abilities among CI users remains of great interest. Our recent investigations into CI users' abilities to perceive emotions and speaker sincerity have often incorporated diverse aspects of auditory proficiency, including pitch discrimination, music processing, and speech intelligibility. The combination of results from these different projects can help shed light on the intricate interplay between speech processing and emotional recognition in CI users. Surprisingly, even when presented with emotional musical stimuli, CI users' proficiency often leaned toward processes related to speech intelligibility, proposing common mechanisms underlying linguistic and affective processes in CI users that do not readily relate to musical skills or pitch sensitivity. Hence, maintaining a clinical focus on speech processing remains crucial, even when exploring affective skills in CI users.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".