A Praat script for unsupervised phoneme segmentation based on spectro-temporal representation
Bibliographic record
Abstract
Unsupervised phoneme segmentation can be useful for corpus-based phonetic research and speech technology. Despite numerous previous studies, software that is straightforward and lightweight to install and execute is hard to find. In response, I present a Praat script which converts input recording to a spectro-temporal representation, measures how the representation differs between either side of each frame, and draws boundaries at peaks of the difference measure. The user can choose from various types of representation (e.g. mel spectrogram, cochleagram) and measure (e.g. cosine distance, spectral transition measure) as well as configure how they are computed and interpreted for peak detection. Experiments suggest that running the script with the log mel spectrogram and spectral transition measure identifies phonetic boundaries at reasonable performance levels: e.g. F-score = 0.771 on the standard TIMIT test set compared with F-score = 0.848 using the Montreal Forced Aligner (McAuliffe et al., 2017) and F-score = 0.837 using a state-of-the-art semi-supervised model based on deep learning (Kreuk et al., 2020). The script is available at the author's website for download as a single file and can be executed via Praat which many phoneticians should already have on their computer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".