Bibliographic record
Abstract
This paper presents Bangla-Align, a toolkit for force-aligning Bangla speech. Forced-aligners are widely used tools for annotating speech at the phone level which greatly aids in increasing the processing power. While forced-aligning tools are freely available for a number of languages including, English, French, Spanish, etc., there is no aligner to process Bangla speech. Bangla-Align is built on top of the Montreal-forced-aligner (McAuliffe et al., 2017) with some Python scripts, which takes the audio recordings (.wav format) and their transcriptions (Text Grid format) as input and then returns Text Grids with phoneme-level annotations for phonetic/acoustic analyses of the audio data. The aligner uses a rule-based and continually developing phoneme dictionary. The aligner currently runs on Linux operating system only via command line interface. Bangla-Align will facilitate phonetic/acoustic research involving Bangla data. Key words: Bangla-align, Bangla speech, forced aligner, align performance
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.064 |
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".