Title
Reward-based learning for virtual neurorobotics through emotional speech processing.
Abstract
Reward-based learning can easily be applied to real life with a prevalence in children teaching methods. It also allows machines and software agents to automatically determine the ideal behavior from a simple reward feedback (e.g., encouragement) to maximize their performance. Advancements in affective computing, especially emotional speech processing (ESP) have allowed for more natural interaction between humans and robots. Our research focuses on integrating a novel ESP system in a relevant virtual neurorobotic (VNR) application. We created an emotional speech classifier that successfully distinguished happy and utterances. The accuracy of the system was 95.3 and 98.7% during the offline mode (using an emotional speech database) and the live mode (using live recordings), respectively. It was then integrated in a neurorobotic scenario, where a virtual neurorobot had to learn a simple exercise through reward-based learning. If the correct decision was made the robot received a spoken reward, which in turn stimulated synapses (in our simulated model) undergoing spike-timing dependent plasticity (SIDE) and reinforced the corresponding neural pathways. Both our ESP and neurorobotic systems allowed our neurorobot to successfully and consistently learn the exercise. The integration of ESP in real-time computational neuroscience architecture is a first step toward the combination of human emotions and virtual neurorobotics.
Year
DOI
Venue
2013
10.3389/fnbot.2013.00008
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
emotional speech processing,reward-based learning,virtual neurorobotics,biological computational model
Neurorobotics,Speech processing,Computational neuroscience,Communication,Computer science,Artificial intelligence,Classifier (linguistics),Reinforcement learning,Software agent,Speech recognition,Affective computing,Robot,Machine learning
Journal
Volume
ISSN
Citations 
7
1662-5218
1
PageRank 
References 
Authors
0.38
18
5