Were we or are we? Perception of reduced function words in spontaneous conversations.
Bibliographic record
Abstract
Spontaneous, reduced pronunciations diverge greatly from citation forms. The quality of a single segment can change, e.g., /b/ in “about” surfacing as an approximant. But sounds, syllables, and entire words can also be deleted (e.g., “do you have time?” as [djutEm] with no acoustic trace of “have”). This work investigates the perception of reduced function words such as “he was” or “we were.” Twenty-two young American English speakers’ spontaneous conversations with close acquaintances were recorded. From these, we selected utterances containing items such as “he’s/he was, we’re/we were, got him/got them.” When hearing an entire utterance, native listeners may clearly perceive “we were,” but on hearing just the “we were” portion, they perceive an unambiguous “we’re.” The portion of the signal presented to listeners is manipulated to determine the contributions of local acoustic cues, speech rate and coarticulation, semantic and syntactic information, and overall bias toward present vs past tense. An auditory and a written task are also compared to separate the contribution of intonation from that of syntax/semantics. These results begin to elucidate the interplay of information sources listeners draw upon when parsing spontaneous speech. Future work will compare to non-native listeners’ perceptions.
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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 | 0.004 |
| 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".