Which cross-language acoustic model to choose: Assessing tonality and phonemic inventories effects
Bibliographic record
Abstract
Cross-language forced alignment techniques, i.e., using a high-resource language acoustic model to automatically align audio files with transcripts for another language, present a promising and feasible approach for expediting the acoustic analysis of low-resource languages (Chodroff, Ahn, and Dolatian 2024). However, the factors influencing the effectiveness of cross-language forced alignment remain underexplored. Mandarin and Japanese share more similarities in phonemic inventories and syllable structures, while Japanese and English are both non-tonal languages. Comparing the alignment results generated by Mandarin and English acoustic models on Japanese audio data can help us explore whether tonality and phonemic inventory similarity have an equal influence on cross-language alignment performance. The present study used 30,975 Japanese audio files from Common Voice datasets as input and Montreal Forced Aligner Mandarin, English, and Japanese acoustic models to generate forced alignment output. In the 1.5 million data points across different comparison pairs, we found that when performing a cross-language alignment on a non-tonal language, a non-tonal language acoustic model is the optimal choice, but a tonal language acoustic model could also work decently if assigning vowels to a falling tone. These findings suggest tone matters in cross-language alignment and offer methodological insights for implementing cross-language alignment in low-resource languages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".