Reliable Speaker Identification Using Multiple Microphones in Ubiquitous Robot Companion Environment

This paper presents a text-independent speaker identification system using multiple microphones on the robot, which is intended for use in human-robot interaction. For the purpose of the best possible classification rate in speaker identification, the individual identification results obtained from multiple microphones on the robot are combined by various combination schemes. The performance improvement has been achieved. Our ultimate goal is to enhance human-robot interaction by improving the recognition performance of speaker identification with multiple microphones on the robot side in adverse distant-talking environments. Various combination schemes obtained high classification accuracy in the ubiquitous robot companion (URC) environment, where the robot is connected to a server through extremely high broadband penetration rate. In conclusion, our speaker identification system can provide human-robot interaction with a reliable basic interface with high classification accuracy.