Effects of Transient Levels of Speech on Auditory Attention Decoding Performance in a <scp>Two‐Speaker</scp> Paradigm
Bibliographic record
Abstract
Stimulus reconstruction decodes the listener's auditory attention through the greater neural tracking of the attended speech over the unattended stream. While acoustic features of speech are vital to the listening task and comprehension, very few studies have analyzed the effects of acoustic features of speech on stimulus reconstruction. This paper investigates approaches of stimulus reconstruction, where correlations between the neurally decoded and actual speech envelopes are calculated from specific speech segments, varying in transient levels as measured by spectral transition measures. Additionally, two methods of calculating correlations were adopted enabling analysis of the effects of relatively lower and higher frequency components of the speech envelope. Correlation after concatenation analysis showed that STM level of only the attended speech affected decoding performance, hinting at a top‐down attentional effect. A bottom‐up effect of salient aspects of speech momentarily dominating neural entrainment was also inferred from the weighted mean of multiple correlations. Future studies on the link between acoustic features of speech and its corresponding neural tracking behavior are suggested. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".