Single-channel vibrotactile feedback for voicing enhancement in trained and untrained perceivers
Bibliographic record
Abstract
Auditory speech intelligibility can be enhanced by integrating information from other modalities, e.g., vision [Sumby & Pollack 1954, J. Acoust. Soc. Am. 26: 212] or direct manual touch [Gick et al. 2008. J. Acoust. Soc. Am. 123: EL72]. There are, nonetheless, many circumstances where shared visual attention may be hard to establish, or where in-person contact may be infeasible (e.g., in a noisy collaborative environment). To test the feasibility of using vibrotactile feedback to enhance intelligibility under noisy conditions, we use a portable voice-coil-based transducer that provides vibrotactile stimulation similar to laryngeal vibrations. Participants were asked to discriminate between minimal pairs in noise. These were distinguished in voicing and vowel height. Participants were asked to wear a vibrator on their fingers, or on their suprasternal notch. We contrasted vibrator placement with different vibration styles, such as a constant vibration on voicing, or vibrations driven by the amplitude envelope of the speech signal. In untrained perceivers we found that vibrotactile feedback increased accuracy regardless of placement. This effect, though significant, was not strong enough to be useful for everyday speech enhancement. These results, and those of a follow-up study with trained perceivers, will be reported. [Funding from NSERC.]
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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 | 0.001 | 0.003 |
| 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) | 0.003 | 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 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".