Bibliographic record
Abstract
In phonetic research, the time needed to manually annotate large-scale data is often prohibitive, and computer-assisted alignment is needed. Forced alignment, an offshoot of automatic speech recognition, is a technique that provides word and phone boundaries of use to linguists. However, this forced alignment systems rely on a dictionary that typically gives canonical pronunciations of words. This study investigates the efficacy of modifying the dictionary of the Montreal Forced Aligner (McAuliffe et al., 2017) to account for a well-attested sociophonetic variation phenomenon, t/d deletion. This is done by aligning the Buckeye Corpus (Kiesling et al., 2006), chosen as it allows comparison of forced alignment results to human transcriber results. Overall, 23 522 words that canonically feature word-final t/d in a consonant cluster were examined. Forced alignment results from this modification were in agreement with human transcribers approximately 71% of the time, close to the 76% agreement of human transcribers (Raymond et al., 2002). These results are promising for future large-scale sociophonetic research in which dictionary modifications can be made to better force align data containing sociophonetic variation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".