Maki Sakamoto（The University of Electro-Communications）
Automatic Estimation of Affective Impressions from a Single Sound-symbolic Word and Word-based Visualization of Perceptual Space
The semantic differential (SD) was mainly developed by the US psychologist Charles E. Osgood in 1950s. Since then, it has been widely applied to capture the affective and cognitive factors of respondents’ attributions to selected concepts and objects on a multidimensional level. This method asks respondents to analyze their affective impression and perceptual experiences one by one. On the other hand, Gestalt psychology implies that human understands external stimuli as whole rather than the sum of their parts. Therefore, we proposed a system that can automatically estimate multidimensional ratings of affective impressions of objects from a single sound-symbolic word that has been spontaneously and intuitively expressed by a user. When a user inputs a sound-symbolic word into the system, the system refers to a database of phonemes and their auditory impressions and calculates ratings in terms of fundamental scales of affective and perceptual experiences. In this talk I will outline the advantage of our method in visualizing our affective impressions of objects and our perceptual space.
Dr. Maki Sakamoto is Professor of Affective Engineering in Department of Informatics, The University of Electro-Communications. She received her Ph.D. in Language and Information Sciences from the University of Tokyo in 2000. From 1998 to 2000, she was an Assistant Professor at the University of Tokyo. In 2000 she moved to the University of Electro-Communications as a Lecturer. She became an Associate Professor in 2004 and a Professor in 2015. She is a vice-director of Artificial Intelligence Exploration Research Center. In 2014, she received the best paper award from the Japanese Society for Artificial Intelligence. Her current research interests are in language, cognition, perception, affective engineering including affective AI. She is a board member of JSAI and JCSS.