Speech based emotional and depressive mental state prediction using Gaussian Process state-space models

2017 Fiscal Year Final Research Report

• Project/Area Number: 15K00243
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Perceptual information processing
• Research Institution: The University of Aizu
• Principal Investigator: Markov Konstantin 会津大学, コンピュータ理工学部, 上級准教授 (80394998)
• Co-Investigator(Kenkyū-buntansha): 松井 知子 統計数理研究所, モデリング研究系, 教授 (10370090)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: Speech emotion / Neural Networks / Gaussian Process / Personality Recogniition / Music emotion

Outline of Final Research Achievements

Estimating the emotional state of a person is an important task with applications in medicine, social interaction as well as in services industry. On the other hand, recognition of the person personality is even more challenging task which includes emotion estimation as one of its components. In this research, we used latest developments and technologies in signal processing and machine learning fields to build several systems for emotion recognition from speech and music as well as text based personality recognition system. The used methods and technologies include Gaussian Processes, non-linear state-space models and deep neural networks.
During the last year of this research, we focused on personality recognition task and built a system based on deep neural networks capable of recognizing the five personality traits with high accuracy. Our findings are published in the IEEE Access journal and several international conferences.

Free Research Field

Signal Processing, Machine Learning