Real-time emotion identification system using voice information

Abstract

Conventional speech emotion identification often uses sentence units as analysis length generally. However, human emotions frequently change their emotions instantaneously when they hear a specific word or keyword that affects each speaker’s emotion, and it is important to capture more detailed emotional expressions for recognition of the emotion. We propose an emotion identification by using acoustic features that analyze speech at each frame, which are shorter than conventional units such as sentences and phrases for capturing and expressing actual emotion. Therefore, we propose a real-time emotion identification system that uses frames as the unit of analysis for acoustic features to the emotion in units of words and morphemes, which are shorter than conventional linguistic units.

Keywords

• speech analysis
• machine learning
• acoustic feature
• emotion estimation
• real-time recognition