Cochlea-scaled spectral entropy predicts rate-invariant intelligibility of temporally distorted sentences
Bibliographic record
Abstract
Some evidence, mostly drawn from experiments using only a single moderate rate of speech, suggests that low-frequency amplitude modulations may be particularly important for intelligibility. Here, two experiments investigated intelligibility of temporally distorted sentences across a wide range of simulated speaking rates, and two metrics were used to predict results. Sentence intelligibility was assessed when successive segments of fixed duration were temporally reversed (exp. 1), and when sentences were processed through four third-octave-band filters, the outputs of which were desynchronized (exp. 2). For both experiments, intelligibility decreased with increasing distortion. However, in exp. 2, intelligibility recovered modestly with longer desynchronization. Across conditions, performances measured as a function of proportion of utterance distorted converged to a common function. Estimates of intelligibility derived from modulation transfer functions predict a substantial proportion of the variance in listeners' responses in exp. 1, but fail to predict performance in exp. 2. By contrast, a metric of potential information, quantified as relative dissimilarity (change) between successive cochlear-scaled spectra, is introduced. This metric reliably predicts listeners' intelligibility across the full range of speaking rates in both experiments. Results support an information-theoretic approach to speech perception and the significance of spectral change rather than physical units of time.
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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 | 0.009 |
| 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) | 0.001 | 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".