Combined effects of form- and meaning-based predictability on perceived clarity of speech.
Bibliographic record
Abstract
The perceptual clarity of speech is influenced by more than just the acoustic quality of the sound; it also depends on contextual support. For example, a degraded sentence is perceived to be clearer when the content of the speech signal is provided with matching text (i.e., form-based predictability) before hearing the degraded sentence. Here, we investigate whether sentence-level semantic coherence (i.e., meaning-based predictability), enhances perceptual clarity of degraded sentences, and if so, whether the mechanism is the same as that underlying enhancement by matching text. We also ask whether form- and meaning-based predictability are related to individual differences in cognitive abilities. Twenty participants listened to spoken sentences that were either clear or degraded by noise vocoding and rated the clarity of each item. The sentences had either high or low semantic coherence. Each spoken word was preceded by the homologous printed word (matching text), or by a meaningless letter string (nonmatching text). Cognitive abilities were measured with a working memory test. Results showed that perceptual clarity was significantly enhanced both by matching text and by semantic coherence. Importantly, high coherence enhanced the perceptual clarity of the degraded sentences even when they were preceded by matching text, suggesting that the effects of form- and meaning-based predictions on perceptual clarity are independent and additive. However, when working memory capacity indexed by the Size-Comparison Span Test was controlled for, only form-based predictions enhanced perceptual clarity, and then only at some sound quality levels, suggesting that prediction effects are to a certain extent dependent on cognitive abilities. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.001 |
| 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".