Acoustic Analysis of Emotional Speech Processed by Hearing Aids
Bibliographic record
Abstract
<p>A talker’s emotional state is one important type of information carried by the speech signal. Past studies have shown that listeners with hearing loss have difficulties identifying vocal emotion. However, there is little research on how much hearing aids may ameliorate these difficulties. The amplitude compression performed by hearing aids makes words easier to recognize, but little is known about how such processing affects the emotional cues carried in the speech signal. The speech materials used in this study were sentences spoken by a young female actor portraying different vocal emotions. These sentences were processed using different hearing aid simulations: a flat 10 dB gain across frequencies; linear gain according to NAL-NL2 targets; fast amplitude compression, and slow amplitude compression. Acoustic analyses of the hearing aid-processed speech showed that the amplitude envelope was flattened by fast amplitude compression, more so for sentences spoken in Angry and Happy conditions than in other emotion conditions. Hearing aid processing using NAL-NL2 targets also led to an increase in the amount of high-frequency energy, more so for sentences spoken in Neutral and Sad conditions than in other emotion conditions. We conclude that amplitude processing by hearing aids is beneficial for audibility but potentially detrimental for emotion understanding.</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.000 | 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 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".