Quality of voices processed by hearing aids: Intra-talker differences
Bibliographic record
Abstract
The effects of hearing aid signal processing depend on the voice characteristics of a talker. For example, we have found that the perceptual and acoustic consequences of hearing aid signal processing vary across talkers and that these effects can be explained, in part, by the acoustic differences between the different voices. However, we find that different utterances spoken by the same talker are also differentially affected by the hearing aid signal processing. In this study, we quantified the acoustic and perceptual consequences of hearing aid signal processing on several utterances spoken by the same talker. The voices were presented in quiet and in the presence of babble noise (10 dB SNR). The voices were processed with varying amounts of frequency compression, wide dynamic range compression and noise suppression (spectral subtraction). We considered intra-talker variability using an objective quality metric (Hearing Aid Sound Quality Index) and perceptual ratings of quality. We analyzed sentences from several different speech corpora, including the Hearing-in-Noise Test (HINT), IEEE and TIMIT. The results showed interactions between the effects of signal processing and the acoustic characteristics of specific utterances spoken by an individual. [Work supported in part by GN ReSound and NSERC]
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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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".