Voice detection and pattern recognition using neck skin vibration signals
Bibliographic record
Abstract
During human phonation, vibrations of the human vocal folds generate glottal periodic acoustic waves, which propagate through the vocal tract and are radiated from the nose and lips. These waves are also structurally transmitted through the bodies to reach different regions of the body. Long-term human voice recording is frequenctly used to facilitate the clinical diagnosis of chronic voice problems. Many devices, such as the microphone, have been criticized for speech privacy disclosure and noise susceptibility, which make long-term voice monitoring difficult to implement. In this dissertation, a portable neck surface accelerometer (NSA) was designed and fabricated to monitor long-term voice use and study the voice features under different vocal conditions, using both subjective and objective voice assessment methods.To investigate the robustness of glottal inverse filtering algorithms, mathematical models of sound propagation in the human respiratory system based on lumped analog circuits were used. The modeling uncertainties of glottal inverse filtering methods were then investigated using synthesized and recorded voice data for different vowels and voice types. The results showed that the accuracies of the supraglottal inverse filtering method varies notably with vowel type and voice type. Consequently, inverse filtering methods are not particularly advantageous for voice monitoring.To investigate the automatic recognition of different voice types (modal, breathy, and pressed), voice data for different voice types were collected from 31 native Canadian English speakers for single vowel phonation using the NSA and the microphone simultaneously. Auditory-perceptual ratings were conducted by five clinically certified speech language pathologists to categorize voice types using the microphone recordings. Congruent NSA samples were analyzed to find trends in extracted voice metrics, such as spectral harmonics, spectral entropy, jitter, and shimmer. An overall classification accuracy greater than 80% was achieved using supervised learning techniques. To investigate the variations of voice quality induced by intensive voice use, a dose-based vocal loading task (VLT) experiment was conducted on nine native Canadian English speakers. The experimental protocol included six successive dose-calibrated VLT sessions followed by a rest session. Voice qualities were evaluated using two standard subjective voice quality assessment methods (the CAPE-V and the SAVRa) at eight time points during this experiment. Results showed that the CAPE-V and the SAVRa ratings consistently followed similar trends. Across-session variations of four microphone features (fundamental frequency, SPL, duty ratio, shimmer) and two NSA features (shimmer, spectral tilt) were closely correlated (rmin>0.7) with each other and showed a general trend of a vocal adjustment period followed by a vocal saturation period. This trend was also consistent with that of the subjective ratings. Overall, the work reported in this thesis supports the novel concept of continuous ambulatory voice monitoring for the detection of vocal fatigue using filted data and machine learning algorithms. The main limitation of this work is the use of signle vowel recordings as opposed to running speech, which will be addressed in future work
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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 | 0.000 | 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 | 0.000 | 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 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".