TY - JOUR
T1 - Automatic non-verbal communication skills analysis
T2 - A quantitative evaluation
AU - Cepero, Álvaro
AU - Clapés, Albert
AU - Escalera, Sergio
N1 - Publisher Copyright:
© 2015 - IOS Press and the authors.
PY - 2015
Y1 - 2015
N2 - The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions.
AB - The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions.
KW - e-Learning
KW - human behavior analysis
KW - multi-modal data description
KW - multi-modal data fusion
KW - non-verbal communication analysis
KW - Social signal processing
UR - http://www.scopus.com/inward/record.url?scp=84918547385&partnerID=8YFLogxK
U2 - 10.3233/AIC-140617
DO - 10.3233/AIC-140617
M3 - Journal article
AN - SCOPUS:84918547385
SN - 0921-7126
VL - 28
SP - 87
EP - 101
JO - AI Communications
JF - AI Communications
IS - 1
ER -