Social robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al., 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al., 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.
10 aauditory mood detection 10 aComputer vision 10 aeducational robot 10 aEducational robots 10 aEmotion recognition 10 aemotional speech database 10 aface detection 10 ahearing 10 ainteractive robotic application 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMood Prototypes 10 aobject recognition 10 aRobotics and Automation Robots 10 asocial mood 10 asocial robot 10 aSpeech 10 aUSA Councils 1 aRuvolo, P. 1 aFasel, I. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/auditory-mood-detection-social-and-educational-robots 01620nas a2200349 4500008004100000020002200041245006600063210006600129260003200195520052200227653001900749653001900768653002800787653003500815653001300850653003900863653001400902653002300916653002400939653001700963653001100980653003900991653002101030653000901051653002501060653001501085653001501100653003001115100001501145700001701160856009301177 2008 eng d a978-1-4244-2661-4 00 aAutomatic cry detection in early childhood education settings 0 aAutomatic cry detection in early childhood education settings aMonterey, CA bIEEE c08/2008 3 aWe present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
10 aAcoustic noise 10 aauditory moods 10 aautomatic cry detection 10 abehavioural sciences computing 10 aDeafness 10 aearly childhood education settings 10 aeducation 10 aEducational robots 10 aEmotion recognition 10 ahuman coders 10 aHumans 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMood 10 apreschool classrooms 10 aPrototypes 10 aRobustness 10 aWorking environment noise 1 aRuvolo, P. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/automatic-cry-detection-early-childhood-education-settings 02582nas a2200373 4500008004100000020002200041245007500063210006900138260003100207520141200238653002801650653003201678653002201710653001601732653001001748653001401758653002301772653002001795653001601815653002801831653002001859653002801879653002301907653001901930653002801949653002201977653003001999653001102029100001502040700001802055700001502073700001702088856010302105 2008 eng d a978-1-4244-2661-4 00 aBuilding a more effective teaching robot using apprenticeship learning 0 aBuilding a more effective teaching robot using apprenticeship le aMonterey, CA bIEE c08/2008 3 aWhat defines good teaching? While attributes such as timing, responsiveness to social cues, and pacing of material clearly play a role, it is difficult to create a comprehensive specification of what it means to be a good teacher. On the other hand, it is relatively easy to obtain examples of expert teaching behavior by observing a real teacher. With this inspiration as our guide, we investigated apprenticeship learning methods [1] that use data recorded from expert teachers as a means of improving the teaching abilities of RUBI, a social robot immersed in a classroom of 18-24 month old children. While this approach has achieved considerable success in mechanical control, such as automated helicopter flight [2], until now there has been little work on applying it to the field of social robotics. This paper explores two particular approaches to apprenticeship learning, and analyzes the models of teaching that each approach learns from the data of the human teacher. Empirical results indicate that the apprenticeship learning paradigm, though still nascent in its use in the social robotics field, holds promise, and that our proposed methods can already extract meaningful teaching models from demonstrations of a human expert.
10 aapprenticeship learning 10 aautomated helicopter flight 10 aAutomatic control 10 aData mining 10 aDelay 10 aeducation 10 aEducational robots 10 aexpert teaching 10 aHelicopters 10 aHuman-robot interaction 10 ahumanoid robots 10 aHumans Learning systems 10 amechanical control 10 arobot teaching 10 aRobotics and Automation 10 aRUBI social robot 10 atime 18 month to 24 month 10 atiming 1 aRuvolo, P. 1 aWhitehill, J. 1 aVirnes, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/building-more-effective-teaching-robot-using-apprenticeship-learning 00392nas a2200133 4500008004100000245004000041210003500081100001500116700001700131700001500148700001500163700001700178856006300195 2007 eng d 00 aThe RUBI Project: A Progress Report 0 aRUBI Project A Progress Report 1 aTanaka, F. 1 aMovellan, J. 1 aTaylor, C. 1 aRuvolo, P. 1 aEckhardt, M. uhttps://rubi.ucsd.edu/content/rubi-project-progress-report