Native Listeners’ Use of Information in Parsing Ambiguous Casual Speech
Bibliographic record
Abstract
In conversational speech, phones and entire syllables are often missing. This can make "he's" and "he was" homophonous, realized for example as [ɨz]. Similarly, "you're" and "you were" can both be realized as [jɚ], [ɨ], etc. We investigated what types of information native listeners use to perceive such verb tenses. Possible types included acoustic cues in the phrase (e.g., in "he was"), the rate of the surrounding speech, and syntactic and semantic information in the utterance, such as the presence of time adverbs such as "yesterday" or other tensed verbs. We extracted utterances such as "So they're gonna have like a random roommate" and "And he was like, 'What's wrong?!'" from recordings of spontaneous conversations. We presented parts of these utterances to listeners, in either a written or auditory modality, to determine which types of information facilitated listeners' comprehension. Listeners rely primarily on acoustic cues in or near the target words rather than meaning and syntactic information in the context. While that information also improves comprehension in some conditions, the acoustic cues in the target itself are strong enough to reverse the percept that listeners gain from all other information together. Acoustic cues override other information in comprehending reduced productions in conversational speech.
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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 | 0.000 |
| 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".