Assessing the importance of several acoustic properties to the perception of spontaneous speech
Bibliographic record
Abstract
Spoken language manifests itself as change over time in various acoustic dimensions. While it seems clear that acoustic-phonetic information in the speech signal is key to language processing, little is currently known about which specific types of acoustic information are relatively more informative to listeners. This problem is likely compounded when considering reduced speech: Which specific acoustic information do listeners rely on when encountering spoken forms that are highly variable, and often include altered or elided segments? This work explores contributions of spectral shape, f0 contour, target duration, and time varying intensity in the perception of reduced speech. This work extends previous laboratory-speech based perception studies into the realm of casual speech, and also provides support for use of an algorithm that quantifies phonetic reduction. Data suggest the role of spectral shape is extensive, and that its removal degrades signals in a way that hinders recognition severely. Information reflecting f0 contour and target duration both appear to aid the listener somewhat, though their influence seems small compared to that of short term spectral shape. Finally, information about time varying intensity aids the listener more than noise filled gaps, and both aid the listener beyond presentation of acoustic context with duration-matched silence.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".