Do Age and Linguistic Status Alter the Effect of Sound Source Diffuseness on Speech Recognition in Noise?
Bibliographic record
Abstract
One aspect of auditory scenes that has received very little attention is the level of diffuseness of sound sources. This aspect has increasing importance due to growing use of amplification systems. When an auditory stimulus is amplified and presented over multiple, spatially-separated loudspeakers, the signal's timbre is altered due to comb filtering. In a previous study we examined how increasing the diffuseness of the sound sources might affect listeners' ability to recognize speech presented in different types of background noise. Listeners performed similarly when both the target and the masker were presented via a similar number of loudspeakers. However, performance improved when the target was presented using a single speaker (compact) and the masker from three spatially separate speakers (diffuse) but worsened when the target was diffuse, and the masker was compact. In the current study, we extended our research to examine whether the effects of timbre changes with age and linguistic experience. Twenty-four older adults whose first language was English (Old-EFLs) and 24 younger adults whose second language was English (Young-ESLs) were asked to repeat non-sense sentences masked by either Noise, Babble, or Speech and their results were compared with those of the Young-EFLs previously tested. Participants were divided into two experimental groups: (1) A Compact-Target group where the target sentences were presented over a single loudspeaker, while the masker was either presented over three loudspeakers or over a single loudspeaker; (2) A Diffuse-Target group, where the target sentences were diffuse while the masker was either compact or diffuse. The results indicate that the Target Timbre has a negligible effect on thresholds when the timbre of the target matches the timbre of the masker in all three groups. When there is a timbre contrast between target and masker, thresholds are significantly lower when the target is compact than when it is diffuse for all three listening groups in a Noise background. However, while this difference is maintained for the Young and Old-EFLs when the masker is Babble or Speech, speech reception thresholds in the Young-ESL group tend to be equivalent for all four combinations of target and masker timbre.
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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 | 0.004 |
| 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 | 0.001 |
| 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".