End-user programming of a social robot by dialog
Robotics and Autonomous Systems
RoboASR: a dynamic speech recognition system for service robots
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Asking rank queries in pose learning
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Human-robot interaction (HRI)1 is one of the main fields in the study and research of robotics. Within this field, dialogue systems and interaction by voice play an important role. When speaking about human-robot natural dialogue we assume that the robot has the capability to accurately recognize what the human wants to transmit verbally and even its semantic meaning, but this is not always achieved. In this article we describe the steps and requirements that we went through in order to endow the personal social robot Maggie, developed at the University Carlos III of Madrid, with the capability of understanding the natural language spoken by any human. We have analyzed the different possibilities offered by current software/hardware alternatives by testing them in real environments. We have obtained accurate data related to the speech recognition capabilities in different environments, using the most modern audio acquisition systems and analyzing not so typical parameters such as user age, gender, intonation, volume, and language. Finally, we propose a new model to classify recognition results as accepted or rejected, based on a second automatic speech recognition (ASR) opinion. This new approach takes into account the precalculated success rate in noise intervals for each recognition framework, decreasing the rate of false positives and false negatives.