Location

Nessmith-Lane Atrium

Session Format

Poster Presentation

Research Area Topic:

Engineering and Material Sciences - Electrical

Co-Presenters and Faculty Mentors or Advisors

Shonda L. Bernadin (Florida State University)

Earl Smith (Georgia Southern University)

Abstract

This research project investigates using fuzzy clustering algorithms for emotion recognition. Emotion recognition has gained significant attention in recent years in applications such as artificial intelligence, human-computer interaction, speech and voice recognition. The ability of a computer or machine to understand human emotion and respond to users in a more human way can lead to significant advances in conversational speech recognition systems, improved quality of life in persons with speech disorders, such as Parkinson’s disease and even in voice response systems, such as Google Voice or Apple’s Siri. Experimental results in this area can inform discovery and innovation of machine intelligence and actionable response algorithms that use physiological methods for characterizing speech. Human emotion is a complex signal that is difficult to characterize analytically. One proposed method for characterizing emotion is to use fuzzy clustering techniques to partition the data into classifications of emotions based on feature similarities. Fuzzy clustering provides a method for organizing data into groups either in unsupervised fashion or based on the selected feature and classifying each group as a different emotion. In this work, an emotional prosody speech dataset is used as input to a fuzzy clustering toolbox to explore underlying structures in the dataset and perform data reduction for optimal feature extraction. The emotion dataset includes fifteen different categories of emotions: happy, elation, sadness, despair, boredom, interest, shame, pride, contempt, disgust, panic, anxiety, hot anger, cold anger, and no emotion. The goal of this research project is to identify a fuzzy clustering technique that will partition the dataset into different categories of emotions. Furthermore, the expected results should illustrate that similar emotions (e.g. sadness and despair) may exhibit similar patterns in classification, and thus may not by recognized as two separate categories by the fuzzy clustering analysis.

Keywords

Georgia Southern University, Research Symposium, Emotion recognition, Fuzzy clustering analysis, Human emotion

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Presentation Type and Release Option

Presentation (Open Access)

Start Date

4-16-2016 2:45 PM

End Date

4-16-2016 4:00 PM

Download

Share

COinS
 
Apr 16th, 2:45 PM Apr 16th, 4:00 PM

Emotion Recognition using Fuzzy Clustering Analysis

Nessmith-Lane Atrium

This research project investigates using fuzzy clustering algorithms for emotion recognition. Emotion recognition has gained significant attention in recent years in applications such as artificial intelligence, human-computer interaction, speech and voice recognition. The ability of a computer or machine to understand human emotion and respond to users in a more human way can lead to significant advances in conversational speech recognition systems, improved quality of life in persons with speech disorders, such as Parkinson’s disease and even in voice response systems, such as Google Voice or Apple’s Siri. Experimental results in this area can inform discovery and innovation of machine intelligence and actionable response algorithms that use physiological methods for characterizing speech. Human emotion is a complex signal that is difficult to characterize analytically. One proposed method for characterizing emotion is to use fuzzy clustering techniques to partition the data into classifications of emotions based on feature similarities. Fuzzy clustering provides a method for organizing data into groups either in unsupervised fashion or based on the selected feature and classifying each group as a different emotion. In this work, an emotional prosody speech dataset is used as input to a fuzzy clustering toolbox to explore underlying structures in the dataset and perform data reduction for optimal feature extraction. The emotion dataset includes fifteen different categories of emotions: happy, elation, sadness, despair, boredom, interest, shame, pride, contempt, disgust, panic, anxiety, hot anger, cold anger, and no emotion. The goal of this research project is to identify a fuzzy clustering technique that will partition the dataset into different categories of emotions. Furthermore, the expected results should illustrate that similar emotions (e.g. sadness and despair) may exhibit similar patterns in classification, and thus may not by recognized as two separate categories by the fuzzy clustering analysis.