APhL aligner: A neural network forced-alignment system
Bibliographic record
Abstract
Forced alignment is increasingly used in phonetics to automatically produce boundaries between words and phones. These boundaries can have significant errors and are often only placed at some predetermined time interval, like every 10 ms. We discuss some potential remedies to these difficulties and test them in a new neural network-based forced alignment system called the APhL Aligner, trained on the TIMIT and Buckeye speech corpora. In part, errors incurred during forced alignment can be attributed to the acoustic models that attempt to separate phones from each other. Even state-of-the-art neural network models struggle to acoustically separate phones. We examine the effect of relaxing the requirement to separate phones by instead training separate detectors for each phone class. Resolving the 10 ms interval difficulty requires a different approach. As with most aligners, we perform a Viterbi-style alignment to align windows of audio spaced at 10 ms to the phone string given by a pronunciation dictionary. We add an additional step, however, and use linear interpolation to determine an intermediate point after the 10 ms interval to place the boundary. We compare the results of these manipulations to the results of the Montreal Forced Aligner, custom-trained on the same 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".