The Effects of Contextual Priming on the Intelligibility of Semantically Ambiguous Degraded Speech
Bibliographic record
Abstract
A significant problem in the area of speech perception is that noisy listening environments often make it difficult to understand what is being said. Furthermore, speech overwhelmingly contains ambiguous words that carry multiple meanings, which can make speech comprehension even more difficult. Previous research has found that spoken sentences containing ambiguous words (e.g. “the woman was told that the mint was used for making coins”) are harder to understand in noise than matched sentences without such words; we call this phenomenon the “ambiguity effect”. The current study examined individuals’ understanding of speech in noisy environments when this speech contains ambiguous words, and how context can influence this understanding. By manipulating the context in which sentences are presented, I examined whether listeners’ interpretation of the sentence can be biased towards a particular meaning, thereby affecting intelligibility. Participants listened to noisy sentences, each of which was preceded by a priming word intended to provide a particular context to the sentence. Two main predictions follow from this study. First, I predict that listeners will be able to understand less from sentences that contain ambiguous words, compared to those that do not. Furthermore, I predict that the priming words will be of greater benefit (particularly related primes) to listeners in understanding sentences with ambiguous words, rather than sentences without ambiguous words. Preliminary findings will be discussed in the presentation. This work will contribute to the current literature concerning how we use semantic information to understand speech in challenging listening environments.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".