Surface Electromyography-Based Speech Detection Amid False Triggers for Artificial Voice Systems in Laryngectomy Patients
Why this work is in the frame
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Bibliographic record
Abstract
Laryngectomy, a surgical intervention for laryngeal cancer, effectively treats the condition but results in the loss of natural speech. Voice restoration post-laryngectomy typically involves manual control, limiting patients’ ability to multi-task while speaking. Surface electromyography (sEMG) offers a hands-free alternative for controlling artificial voice systems. However, challenges arise from daily, orofacial activities like chewing or coughing, activating the same muscles used for sEMG control, potentially causing false triggers. To address this, we perform a detailed analysis of facial and neck muscles during speech and non-speech activities to identify potential false triggers for sEMG-controlled artificial voice systems. We propose a five-step algorithm to prepare noisy sEMG data for analysis and to detect accurate speech onset and termination times within the muscle activity. A two-stage classification approach is suggested to effectively distinguish speech from non-speech activities. The classifier in the first stage detects the presence of any activity versus non-activity with an F1-score of 95.8%, while the classifier in the second stage recognizes speech among other activities with an F1-score of 96.3%. This research marks a significant advancement in differentiating speech from other daily activities, thereby minimizing false triggers in sEMG-controlled artificial voice systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it