Machine learning-based vowel classification using two ultrasonic transducers in pulse-echo mode
Bibliographic record
Abstract
This study investigated a simpler alternative to cumbersome multisensor systems (e.g., multi-channel EMG) for articulatory gesture recognition. The method was validated in a vowel classification task analyzing pulse-echo waveforms from articulatory muscles. Pulse-echo signals for five Japanese vowels and a neutral state were acquired from two male subjects (20s). Two 5-MHz ultrasonic transducers (UTs) with a diameter of 10 mm were placed on the chin near the digastric muscle. Signals were acquired using two pulser-receivers (pulse repetition frequency: 2 kHz) and an oscilloscope (sampling frequency: 625 MHz). Five 6-s trials (2 s neutral, 2 s vowel, 2 s neutral) were conducted for each vowel. Spatial features were obtained using a discrete wavelet transform and mean absolute value, while temporal features were derived from their time differences. After feature selection, k-nearest neighbors (kNN) and linear discriminant analysis (LDA) classifiers were trained. Performance was validated using trial-based 5-fold cross-validation. For comparison, the same experiments and analyses were conducted with UTs on both cheeks near the masseter muscles. The LDA classifier achieved higher accuracies of 94% (digastric muscle) and 83% (masseter muscle) compared to the kNN classifier with 80% and 70%, respectively. Similar results from the second subject support the method's effectiveness.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".