Voice-Controlled In-Vehicle Systems: Effects of Voice-Recognition Accuracy in the Presence of Background Noise
Bibliographic record
Abstract
This paper presents initial findings from a driving simulator studyThis paper presents initial findings from a driving simulator studycomparing user responses to a noise-robust voice-controlled system while drivingto a noise-sensitive one in the presence of background noise. Twenty participantsinteracted with both noise-sensitive and noise-robust simulated voice-controlledinfotainment systems while driving under three background noise conditions (nonoise, music, and children). While both systems were viewed as useful andsatisfying, user acceptance was affected by background noise with the noisesensitivesystem, but not the noise-robust one. There was also no evidence that useracceptance was calibrated by having background noise as a context for varyinglevels of accuracy. No significant differences were observed between the twosystems in driving performance metrics analyzed (average speed, speed variability,and standard deviation of lane position), but the use of either system affecteddriving performance compared to baseline driving. A larger sample size at the endof this study along with the analysis of a larger set of performance metrics willprovide further insights.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".