Perception of word boundaries in spontaneous speech
Bibliographic record
Abstract
Studies of lab speech have identified several acoustic variables that listeners use to identify word boundaries (e.g. allophonic variation, segment duration), however spontaneous speech may not contain the same acoustic signals. Using data from a recent study we tested whether disambiguating information is also available to listeners under more typical conditions. 15 pairs of target phrases only differing in the placement of word boundaries, e.g., grey#day vs. grade#A were elicited under read (146 tokens) and spontaneous speech conditions (316 tokens) from multiple talkers who were not aware of the ambiguities. Phrases were played in isolation to 30 Native English listeners in a 2AFC segmentation task (e.g. grey day vs. grade A). Accuracy was above chance for both read (80.9%) and spontaneous speech (73.1%). Accuracy varied according to the critical consonant type and was highest for voiceless stops followed by nasals, voiced stops, fricatives and clusters. Only two items (pierced ears vs. peer steers and beef eater vs. bee feeder) were at chance for spontaneous speech. Measures of word-initial lengthening were good predictors of performance but listeners outperformed predictions from these measures indicating they weren’t the only cues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".