Spike-timing and mean-rate coding of the temporal fine structure and envelope cues in real words
Bibliographic record
Abstract
A number of studies over the past decade have argued for the importance of temporal fine structure (TFS) cues for the perception of consonants. However, recent investigations indicate that TFS cues from consonants may largely be converted into envelope (ENV) cues by narrowband cochlear filtering, such that these cues are conveyed by the mean-rate response of auditory nerve fibers rather than spike-timing cues. However, these studies used nonsense VCV syllables, and this result may not generalize to real words in which the patterns of ENV and TFS cues may be substantially different, and in which lexical context may play a role. In this study, we used a computational model of the auditory periphery and neural-based speech intelligibility predictors to investigate the TFS and ENV representation of real words from the NU-6 database. Spike-timing and mean-rate cues were evaluated for “auditory chimaeras” created from this database, in which the TFS of one signal is mixed with the ENV of another. The results indicate that the chimaera processing has a bigger impact in general on the mean-rate representation of phonemes than on the spike-timing representation, and inclusion of the spike-timing cues gives better predictions of phoneme perception. [Funded by NSERC of Canada.]
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".