Fusion of Gesture and Speech for Increased Accuracy in Human Robot Interaction

Abstract

An approach for decision-level fusion for gesture and speech based human-robot interaction (HRI) is proposed. A rule-based method is compared with several machine learning approaches. Gestures and speech signals are initially classified using hidden Markov models, reaching accuracies of 89.6% and 84% respectively. The rule-based approach reached 91.6% while SVM, which was the best of all evaluated machine learning algorithms, reached an accuracy of 98.2% on the test data. A complete framework is deployed in real time humanoid robot (NAO) which proves the efficacy of the system.