Channel segregation improves perception of speech with temporally desynchronized bands.
Bibliographic record
Abstract
Stilp et al. [J. Acoust. Soc. Am. 122, 2971 (2007)] investigated the intelligibility of temporally desynchronized bands of speech and concluded that listeners’s resilience to temporal asynchrony may possibly be due to differences in fundamental frequency (f0) across syllables which may help in segregating bands. This hypothesis is tested in two experiments in temporally desynchronized bands but with spectral manipulations designed to either aid or inhibit stream segregation. Seven-syllable sentences were synthesized at three different speaking rates and processed by four nonoverlapping 1/3-octave filters. Onsets of the lowest- and highest-frequency bands were parametrically delayed. In Experiment 1, f0 in delayed bands was uniformly elevated using pitch-synchronous overlap add synthesis. In the control condition (no f0 manipulation), intelligibility was nonmonotonic with delay across speaking rates with local minima corresponding to the duration of one syllable. The two-speaker manipulation decreased spectral similarity across bands making intelligibility more resilient to temporal distortion. In Experiment 2, f0 contours were flattened. Intelligibility was uniformly poorer than in the control condition; the increased spectral similarity compromised listeners’ ability to segregate information from band pairs at various delays. Acoustic measures of potential information, absent explicit linguistic information, reinforce the strong relationship between spectral predictability and intelligibility. Overall, results provide support that channel segregation plays an important role in the perception of temporally desynchronized bands. [Supported by NIDCD]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".