Analyzing crowdsourced ratings of speech-based take-over requests for automated driving

Bazilinskyy, P., De Winter, J. C. F.

Applied Ergonomics, 64, 56–64 (2017)

ABSTRACT Take-over requests in automated driving should fit the urgency of the traffic situation. The robustness of various published research findings on the valuations of speech-based warning messages is unclear. This research aimed to establish how people value speech-based take-over requests as a function of speech rate, background noise, spoken phrase, and speaker’s gender and emotional tone. By means of crowdsourcing, 2,669 participants from 95 countries listened to a random 10 out of 140 take-over requests, and rated each take-over request on urgency, commandingness, pleasantness, and ease of understanding. Our results replicate several published findings, in particular that an increase in speech rate results in a monotonic increase of perceived urgency and commandingness. The female voice was preferred over a male voice when there was a high level of background noise, a finding that contradicts the literature. Moreover, a take-over request spoken with Indian accent was found to be easier to understand by participants from India compared to participants from other countries. Our results replicate effects in the literature regarding speech-based warnings, and shed new light on effects regarding background noise, gender, and nationality. The results may have implications for the selection of appropriate take-over requests in automated driving. Additionally, our study demonstrates the promise of crowdsourcing for testing human factors and ergonomics theories with large sample sizes.