scholarly journals A Chat System Based on Emotion Estimation from Text and Embodied Conversational Messengers

2005 ◽  
pp. 535-538 ◽  
Author(s):  
Chunling Ma ◽  
Helmut Prendinger ◽  
Mitsuru Ishizuka
Keyword(s):  
Emotion Estimation

scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

HYBRID FUSION OF FACE AND SPEECH INFORMATION FOR BIMODAL EMOTION ESTIMATION

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Krishna Mohan Kudiri
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Facial Expressions ◽  
Detection System ◽  
Support Vector ◽  
Emotion Detection ◽  
Fusion Technique ◽  
Speech Information ◽  
Hybrid Fusion ◽  
Emotion Estimation

Estimation of human emotions during a conversation is difficult using a computer. In this study, facial expressions and speech are used in order to estimate emotions (angry, sad, happy, boredom, disgust and surprise). A proposed hybrid system through facial expressions and speech is used to estimate emotions of a person when he is engaged in a conversational session. Relative Bin Frequency Coefficients and Relative Sub-Image-Based features are used for acoustic and visual modalities respectively. Support Vector Machine is used for classification. This study shows that the proposed feature extraction through acoustic and visual data is the most prominent aspect affecting the emotion detection system, along with the proposed fusion technique. Although some other aspects are considered to be affecting the system, the effect is relatively minor. It was observed that the performance of the bimodal system was lower than the unimodal system through deliberate facial expressions. In order to deal with the problem, a suitable database is used. The results indicate that the proposed system showed better performance, with respect to basic emotional classes than the rest.

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