Ms. LIU, Yuning (3rd-year doctoral student in Unoki Lab, Human Information Science Research Area) received IEEE Nagoya Section Student Award 2023 in Joint conference of Hokuriku chapters of Electrical and information Societies 2023.

The Joint conference of Hokuriku chapters of Electrical and information Societies (JHES) is the largest academic conference in Hokuriku supporting diverse research and development. It is a joint conference sponsored by the Hokuriku Sections of The Institute of Electrical Engineers of Japan, The Institute of Electronics, Information and Communication Engineers, The Institute of Image Information and Television Engineers, Japanese Society for Medical and Biological Engineering, Information Processing Society of Japan, The Society of Instrument and Control Engineers, Acoustical Society of Japan, and The Institute of Electrical Installation Engineers of Japan, as well as IEEE Nagoya Section, the real appeal of the conference is the interdisciplinary exchange that transcends fields of study, which is not possible at general conferences. The conference is used as a venue for co-creation of knowledge and value among universities, technical colleges, and industries in the Hokuriku region and for fostering innovative human resources.
IEEE Nagoya Section has established the "Student Award " as a system to recognize outstanding student paper presentations at the conference. The award was given to Ms. LIU, Yuning for her paper presented at JHES 2023 held online at Kanazawa Institute of Technology last September.

＊Reference：JHES2023

■Date Awarded
November 8, 2023

■Title
Emotion Prediction based on Conversation Entrainments

■Authors
Liu Yuning , Unoki Masashi

■Abstract
The study explores entrainment, the phenomenon of speakers adapting to each other, in the context of emotional expression during conversations. By investigating various dimensions, including phonetics and emotional states, and analyze whether entrainment patterns differ across emotions and its potential to enhance emotion prediction accuracy. The results indicate that entrainment is effective in emotion prediction task, and by the combination with traditional acoustic features enhances predictive capabilities, show the potential of the entrainment to make machines interacting with humans using speech with emotional intelligence by adding it as a functionality.

■Comment
I am very honored to receive this award in JHES2023, the largest academic gathering in Hokuriku. I believe that I was able to receive this award thanks to the various opinions and support I received from my supervisor, Professor Masashi Unoki, and all the members of my laboratory. I would like to use this award as encouragement to work even harder on entrainment phenomena in dialogue in the future.

January 11, 2024