Examining the effect of infotainment auditory-vocal systems' design components on workload and usability

Biondi, FN; Getty, D; Cooper, JM; Strayer, DL · 2019 · publications_jsonl

DOI: 10.1016/j.trf.2019.02.006

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Summary

This study investigates how specific design components of in-vehicle auditory-vocal infotainment systems—specifically menu depth, system delay times, and speech recognition accuracy—affect driver workload and usability. While voice interfaces are promoted as safer alternatives to visual-manual controls because they reduce eyes-off-road time, prior research indicates they impose high cognitive loads and require long interaction times. This research addresses the lack of systematic, real-world analysis regarding which specific design features drive these negative outcomes. The researchers conducted an on-road study involving 120 participants driving twelve different vehicles equipped with distinct auditory-vocal systems. Participants performed voice dialing and voice tuning tasks while driving on a residential route. The study measured three primary design components for each system: menu depth (number of interaction steps), total delay time (processing time between command and response), and system accuracy (success rate of command recognition). Dependent variables included objective task duration and subjective measures of mental workload (NASA-TLX), usability (System Usability Scale), and user sentiment (polarity analysis). A hierarchical regression analysis was used to determine the predictive weight of each design component. The results revealed that total delay time was a significant predictor of task duration for voice dialing, explaining 39% of the variance. Longer delay times were also strongly associated with higher subjective mental workload, increased perceived complexity, and lower ease-of-use ratings. Furthermore, longer delays directly reduced usability scores and generated more negative user sentiment. For voice tuning tasks, both total delay time and menu depth significantly predicted mental workload and usability metrics. Surprisingly, system accuracy was not a significant predictor of workload or task duration, likely due to participants’ extensive practice allowing them to adapt to specific system quirks. The predictive model for voice dialing task duration was validated using data from seven additional vehicles, showing no significant difference between predicted and actual durations. The findings indicate that system responsiveness is a critical determinant of driver distraction and user experience in auditory-vocal interfaces. Contrary to the hypothesis that delays might provide "cognitive slack" for drivers to refocus on the road, longer delays increased task duration and mental demand. Menu depth also negatively impacted workload for tuning tasks. These results suggest that automotive manufacturers should prioritize reducing system latency and simplifying menu structures to improve safety and usability. The study provides a validated framework for assessing in-voice systems, highlighting that low accuracy is less detrimental than poor responsiveness and complex navigation structures in real-world driving conditions.

Key finding

Voice-system delay time and menu depth, but not recognition accuracy, drive increased mental workload and longer task durations, with delay time alone explaining a substantial share of variance in usability and sentiment ratings across twelve production infotainment systems.

Methodology

on_road

Sample size: 120 drivers (54 female), age 21-36 (M=25); 24 per vehicle across 12 vehicles; planned-missing design; 7 additional vehicles for validation

Provenance

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Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• voice interaction
• auditory
• mental demand
• infotainment
• temporal

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Applied Guidance: design guidelines
• Empirical Findings: self report data
• Theoretical Contribution: theory or model