Processing reduced speech across languages and dialects
Bibliographic record
Abstract
Normal, spontaneous speech utilizes many reduced forms. Consonants in spontaneous speech frequently have a different manner or voicing than would be expected in clear speech (e.g. /d/ and /ŋ/ in “you doing” both being realized as glides or /dȝ/ in “just” as a fricative), and near or complete deletions are also common (e.g. the flap in “a little”). Thus, listeners encounter and must process such pronunciations frequently. When speakers and listeners do not share the same dialect or native language, such reductions may hinder processing more than for native listeners of the same dialect. The current work reports a lexical decision experiment comparing listeners’ processing of reduced vs. careful stops (e.g. /g/ in”baggy” pronounced as an approximant or as a stop), by several groups of listeners. Results show that listeners from both Arizona and Alberta, Canada can recognize speech by an Arizona speaker with reduced stops, but they recognize the words more easily when stops are clearly articulated. Speech style of the preceding frame sentence has little effect, suggesting that both groups can process the stops regardless of whether surrounding context leads them to expect reduced stops. Additional data from second-language learners and bilingual listeners is currently being collected.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".