Speech Recognition and In-Vehicle Telematics Devices: Potential Reductions in Driver Distraction

Speech Recognition is frequently cited as a potential remedy to distraction resulting from drivers' operation of in-vehicle devices. This position typically assumes that the introduction of speech recognition will result in reduced cognitive workload and improved driving performance. Past research neither fully supports nor fully discounts this assumption. However, it is difficult to compare many of these studies, due to differences in device operation tasks, the pacing of those tasks, speech recognition system performance, and system interface designs. In an effort to directly address the effect of voice recognition on driver distraction, the present authors developed a capability to manipulate the performance characteristics of a speech recognition system through a Wizard of Oz speech recognition system and installed this system in a simulated driving environment. The sensitivity of the simulated driving environment and speech recognition accuracy manipulation were evaluated in an initial study comparing driver cognitive workload and driving performance during self-paced simulated operation of a personal digital assistant (PDA) during no PDA use, manual control of the PDA, and speech control of the PDA. In the Speech PDA condition, speech recognition accuracy was varied between drivers. Analysis of drivers' emergency braking response times and rated cognitive workload revealed significantly lower cognitive demand and better performance in the No PDA condition when compared to the Manual PDA condition. The Speech PDA condition resulted in response times and rated cognitive workload levels that were between the No PDA and Manual PDA conditions, but not significantly different from either of these conditions. Further analysis of emergency braking performance revealed a non-significant trend towards better performance in conjunction with higher speech recognition accuracy levels. The potential for reducing driver distraction through the careful development and evaluation of speech recognition systems is discussed.