Comparing language-specific and cross-language acoustic models for low-resource phonetic forced alignment
Bibliographic record
Abstract
Phonetic forced alignment can greatly expedite spoken language analysis by providing automatic time alignments at the word and phone levels. In the case of low-resource languages, it remains an open question whether phone-level forced alignment will be more successful with a small language-specific acoustic model or a high-resource cross-language acoustic model. The present study directly compared the forced alignment performance of language-specific and cross-language acoustic models using the Urum and Evenki datasets from the DoReCo Corpus. We evaluated six language-specific acoustic models trained with 5, 10, 15, 20, 25, or approximately 70 minutes of language-specific speech data against four English-based cross-language acoustic models that differed in size and accent homogeneity (large Global English or homogeneous American English of varying data amounts). Acoustic models were developed or obtained from the Montreal Forced Aligner and evaluated against held-out manually aligned phone boundaries. Overall, the Global English model and the larger language-specific acoustic models were competitive with one another and outperformed the homogeneous cross-language and smaller language-specific acoustic models. From this analysis, we recommend that researchers use a language-specific model with at least 25 minutes of actual speech (not just recording duration) or a large, diverse cross-language acoustic model for low-resource forced alignment.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".