Vocal emotion identification by older listeners with hearing loss
Bibliographic record
Abstract
A speaker’s emotional state is one important type of information carried by the speech signal. Numerous studies have been conducted on younger and older normal-hearing listeners’ ability to identify vocal emotions. Much less is known about how hearing loss affects emotion identification, and whether listeners with hearing loss use similar acoustic cues as normal-hearing listeners to identify emotions. The purpose of this study was to evaluate vocal emotion identification performance by younger normal-hearing listeners and older listeners who use hearing aids, and to examine possible acoustic cues used by listeners to distinguish between emotions. The results showed that older listeners with hearing loss performed much worse in a quiet listening environment (38.9%) than young normal-hearing listeners in quiet (84.7%) or in noise (68.4%), and that the use of hearing aids did not improve their performance significantly. Consideration of the pattern of identification errors and the acoustics of the stimuli suggested that the two listener groups relied on different F0-related cues to distinguish emotions: young listeners related on F0 mean and F0 contour, while older listeners relied mainly on F0 mean. Understanding how listeners with hearing loss identify emotion would provide guidance for developing rehabilitative strategies for hearing loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".