Improved vowel labeling for prenasal merger using customized forced alignment
Bibliographic record
Abstract
Forced alignment is a popular technique for gaining phone-level audio transcriptions, but the pronunciation dictionaries used by it are typically based on standard varieties of US English, leading to errorful outputs for non-standard varieties. We employ a customized pronunciation dictionary with the Montreal Forced Aligner to increase labeling accuracy of the prenasal merger (a.k.a. pin-pen merger) in Southern US English. We allow the aligner to choose between IH (/ɪ/) and EH (/ɛ/) in words where the merger is expected, rather than enforcing a standard, unmerged pronunciation. We examine the tokens reclassified from EH to IH when using the new dictionary, and we use formant values to study the acoustic separation (measured by Pillai scores and Euclidean distances between centroids) between vowel formant clusters. When applied to the Digital Archive of Southern Speech (DASS), we find that the modification increases the separation between the prenasal allophones of IH and EH, and also that the proportion of prenasal EH tokens reclassified to IH is correlated with the original degree of separation between prenasal IH and EH for each DASS speaker. K-means clustering is also used to show the modification yields more accurate phonetic transcriptions, measured by increased precision and recall.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".