Automotive User Interfaces and Interactive Vehicular Applications

It happens that there is a large literature on attention and
comprehension in the synthetic speech context and in the
dialogue context. Different levels of linguistic representation
affect or appear to be affected by tasks that demand
attention; in particular, syntactic and semantic factors appear
to have an effect on human performance in tasks that
involve interaction with both synthetic and natural speech.
There is also a large literature on computing psycholinguistic
complexity and different levels of linguistic representation;
divided-attention tasks such as driver multitasking permit
this work to be leveraged in an applied context.
Given this scientific background, we have proposed an overall
system design for in-vehicle spoken dialogue complexity
management. We have also identified some of the design
and data collection challenges that need to be overcome in
pushing this agenda forward. As yet there is no publicly
available corpus that relates transcribed speech data to user
task performance and driving performance. However, the
technical challenges to this, though significant, are not insurmountable.
The final step is the design and construction
of variable-complexity in-car dialogue systems.
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