We present a case-study analysing the prosodic contours and salient word markers of a small corpus of robot-directed speech where the human participants had been asked to talk to a socially interactive robot as if it were a child. We assess whether such contours and salience characteristics could be used to extract relevant information for the subsequent learning and scaffolding of meaning in robots. The study uses measures of pitch, energy and word duration from the participants speech and exploits Pierrehumbert and Hirschberg's theory of the meaning of intonational contours which may provide information on shared belief between speaker and listener. The results indicate that 1) participants use a high number of contours which provide new information markers to the robot, 2) that prosodic question contours reduce as the interactions proceed and 3) that pitch, energy and duration features can provide strong markers for relevant words and 4) there was little evidence that participants altered their prosodic contours in recognition of shared belief. A description and verification of our software which allows the semi-automatic marking of prosodic phrases is also described.
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