  Voice-based communication is often cited as one of the most `natural' ways in
which humans and robots might interact, and the recent availability of accurate
automatic speech recognition and intelligible speech synthesis has enabled
researchers to integrate advanced off-the-shelf spoken language technology
components into their robot platforms. Despite this, the resulting interactions
are anything but `natural'. It transpires that simply giving a robot a voice
doesn't mean that a user will know how (or when) to talk to it, and the
resulting `conversations' tend to be stilted, one-sided and short. On the
surface, these difficulties might appear to be fairly trivial consequences of
users' unfamiliarity with robots (and \emph{vice versa}), and that any problems
would be mitigated by long-term use by the human, coupled with `deep learning'
by the robot. However, it is argued here that such communication failures are
indicative of a deeper malaise: a fundamental lack of basic principles --
\emph{priors} -- underpinning not only speech-based interaction in particular,
but (vocal) interactivity in general. This is evidenced not only by the fact
that contemporary spoken language systems already require training data sets
that are orders-of-magnitude greater than that experienced by a young child,
but also by the lack of design principles for creating effective communicative
human-robot interaction. This short position paper identifies some of the key
areas where theoretical insights might help overcome these shortfalls.
