Automatic alignment for New Englishes: Applying state-of-the-art aligners to Trinidadian English
Bibliographic record
Abstract
While forced alignment has become an essential part of data processing in phonetic research, state-of-the-art aligners are often exclusively tailor-made for majority dialects, such as American English(es). This paper provides the first in-depth investigation into the reliability of popular pre-trained aligners in New Englishes-the nativized, postcolonial Englishes spoken world-wide. Using manually aligned data from Trinidadian English, the paper examines popular aligners [Forced Alignment and Vowel Extraction (FAVE), Munich Automatic Segmentation (MAUS), and the Montreal Forced Aligner (MFA)] and their performances in automatically segmenting Trinidadian speech. Results show that, first, only specific aligners (FAVE and MFA) can provide alignment that is comparable to that in the training varieties and, to a smaller degree, general human inter-rater uncertainty. Second, even well-performing aligners introduce bias toward their training varieties: the aligners systematically produce more erroneous alignments of Trinidadian English-specific vowels, for which they have no acoustic models. The findings suggest that phonetic research on New Englishes can benefit from pre-trained, state-of-the-art aligners, but that further manual data processing may generally be required to minimize errors in the analysis of non-majority dialect data.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".