Over the past few years, there has been a shift in automotive human machine interfaces from visual-manual interactions (pushing buttons and rotating knobs) to speech interaction. In terms of distraction, the industry views speech interaction as a “low” or “no” cost way of providing advanced information to the driver. However, limited data exist on how older and more technologically adverse operators currently experience difficulty learning command based syntaxes for speech interaction. These syntaxes are often unfamiliar, cumbersome and stepwise. The ultimate acceptability of speech based systems for in-vehicle interactions therefore lies in the development of more intuitive naturalistic modes of interaction. For example, when considering in vehicle-based speech interfaces, one would like to know how speech, language, and dialogue can be most effectively used by drivers of different ages and technological backgrounds to perform information retrieval tasks. To optimize this technology for groups with different technological backgrounds and demographics, this project investigated the extent to which a conversational spoken language speech-interface can enhance the driver and passenger experience while minimizing distraction and confusion.
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