A comparison of voice quality measures across in-ear, outer-ear, and standard microphone recordings
Bibliographic record
Abstract
Voice quality provides significant insights into one’s health. Recent advancements in intra-aural devices have enabled the longitudinal monitoring of speech and its changes. These wearables can record speech from in-ear microphones (IEMs) and outer-ear microphones (OEMs). The laboratory gold standard of speech recording uses a high-sensitivity low-noise microphone placed in front of the mouth to capture signals accurately. Speech recorded inside an occluded ear canal differs considerably from this due to the bone-and-tissue conduction and the effect of ear occlusion. OEMs record speech transmitted solely through air like the standard microphones but are more influenced by indirect air conduction due to their positioning. This study compares voice quality measures across IEM, OEM, and the standard microphone (REF) using an open-access database. We employed linear mixed-effect modeling to analyze the effects of different microphone recordings on these metrics. Results indicate that while pitch control remains relatively intact, IEM and OEM exhibit varying deviations from REF. Overall, OEM resembles REF in jitter, whereas IEM is higher. IEM resembles REF in shimmer, while OEM is lower. For HNR, both IEM and OEM are higher than REF. Sex-based difference was also observed, and the correlation between microphone differences and fundamental frequency was explored.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".