Predicting opportune moments for in-vehicle proactive speech services

Auditory-verbal or speech interactions with in-vehicle information systems have became increasingly popular. This opens up a whole new realm of possibilities for serving drivers with proactive speech services such as contextualized recommendations and interactive decision-making. However, prior studies have warned that such interactions may consume considerable attentional resources, thus degrade driving performance. This work aims to develop a machine learning model that can find opportune moments for the driver to engage in proactive speech interaction by using the vehicle and environment sensor data. Our machine learning analysis shows that opportune moments for interruption can be conservatively inferred with an accuracy of 0.74.