The effects of microphone positioning in hearables on voice quality and F0 measurements
Bibliographic record
Abstract
Voice quality and fundamental frequency (F0) metrics are important indicators of motor function and hold promise for health monitoring. Recent advances in hearables have enabled the longitudinal monitoring of speech production and its changes. Hearables can record speech from in-ear microphones (IEMs) and outer-ear microphones (OEMs), but it remains unclear how these measurements from hearables compare to the laboratory gold standard, a microphone placed in front of the mouth. This study examines voice quality and F0 measurements across the IEM, OEM, and the standard method (REF) using parallel recordings. Results showed that the IEM introduced more variability overall; increases in jitter, harmonic-to-noise ratio (HNR), F0 maximum, and standard deviation and decreases in F0 minimum were seen for females. Decreased shimmer and increased HNR were seen in the OEM. The causes of these differences were discussed. The findings indicate that the hearable-based measurements may not align with REF standards, suggesting the need for new standards specific to hearables. Preliminary observations of sex-based differences require further investigation with adequately powered and balanced samples to determine their significance and generalizability. Future research should further explore factors such as occlusion effect and sex-specific differences (e.g., F0 range) in the relationship between hearables and REF measurements.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".