Abstract
The continuous increase of social robots is leading quickly to the cohabitation of humans and social robots at homes. The main way of interaction in these robots is based on verbal communication. Usually social robots are endowed with microphones to receive the voice signal of the people they interact with. However, due to the principle the microphones are based on, they receive all kind of non verbal signals too. Therefore, it is crucial to differentiate whether the received signal is voice or not.
In this work, we present a Voice Activity Detection (VAD) system to manage this problem. In order to achieve it, the audio signal captured by the robot is analyzed on-line and several characteristics, or statistics, are extracted. The statistics belong to three different domains: the time, the frequency, and the time-frequency. The combination of these statistics results in a robust VAD system that, by means of the microphones located in a robot, is able to detect when a person starts to talk and when he ends.
Finally, several experiments are conducted to test the performance of the system. These experiments show a high percentage of success in the classification of different audio signal as voice or unvoice.
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Alonso-Martin, F., Castro-González, Á., Gorostiza, J.F., Salichs, M.A. (2013). Multidomain Voice Activity Detection during Human-Robot Interaction. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds) Social Robotics. ICSR 2013. Lecture Notes in Computer Science(), vol 8239. Springer, Cham. https://doi.org/10.1007/978-3-319-02675-6_7
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DOI: https://doi.org/10.1007/978-3-319-02675-6_7
Publisher Name: Springer, Cham
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