We demonstrate the utility of speech prosody as a feedback mechanism in a machine learning system. We have constructed a reinforcement learning system for our humanoid robot Nico, which uses prosodic feedback to refine the parameters of a social waving behavior. We define a waving behavior to be an oscillation of Nico's elbow joint, parameterized by amplitude and frequency. Our system explores a space of amplitude and frequency values, using q-learning to learn the wave which optimally

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... a human tutor. To estimate tutor feedback in real-time, we first segment speech from ambient noise using a maximum-likelihood voice-activation detector. We then use a k-Nearest Neighbors classifier, with k=3, over 15 prosodic features, to estimate a binary approval/disapproval feedback signal from segmented utterances. Both our voiceactivation detector and prosody classifier are trained on the speech of the individual tutor. We show that our system learns the tutor's desired wave, over the course of a sequence of trialfeedback cycles. We demonstrate our learning results for a single speaker on a space of nine distinct waving behaviors. Index Terms -speech prosody, human-robot interaction, reinforcement learning, socially-guided machine learning.