Less than careful speech: Exploring the roles of target duration and time varying intensity in spoken language processing
Bibliographic record
Abstract
Despite substantial phonetic variation in the production of natural speech, listeners are quite adept at processing varying speech signals. Further, the recognition of spontaneous speech is a multifaceted problem where listeners are drawing information from many sources in the recognition process. Previous research has begun to outline the influence of both syntactic and semantic contributions to spoken word recognition; however, it has been argued (Ernestus et al., 2002; van de Ven et al., 2012) that such cues reach a ceiling in their contributions, and that acoustic information also aids the listener when processing language. There has been limited discussion on what information encoded within a given speech signal lends itself to more effectively processing spontaneous speech. The present study contributes to such a discussion, using responses from 85 participants in modified Cloze tasks and speech in noise manipulations, where results serve as a starting place to describe the contributions of duration and intensity in natural speech (as well as their relative effects on spoken language processing). The introduction of particular acoustic information—over and above syntactic and semantic cues—has been observed within this study as a statistically significant contributor to more effective spoken language processing.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".