• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1364-1369
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• 2018

Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance.

• Journal of IKEEE
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• v.24 no.4
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• pp.998-1003
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• 2020

In this paper, an emotion training method using emotional adaptation is proposed to improve the performance of the existing emotion recognition system. For emotion adaptation, an emotion speech model was created from a speech model without emotion using a small number of training emotion voices and emotion adaptation methods. This method showed superior performance even when using a smaller number of emotional voices than the existing method. Since it is not easy to obtain enough emotional voices for training, it is very practical to use a small number of emotional voices in real situations. In the experimental results using a Korean database containing four emotions, the proposed method using emotional adaptation showed better performance than the existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.528-533
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• 2010

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variation on the speech signal and the speech recognition system were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, a training method that cover the speech variations is proposed to develop the emotionally robust speech recognition system. Experimental results from the isolated word recognition using HMM showed that propose method reduced the error rate of the conventional recognition system by 28.4% when emotional test data was used.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.150-155
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• 2004

In this paper, an emotion recognition method using speech signal is presented. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. The proposed recognizer have each codebook constructed by using the wavelet transform for the emotional state. Here, we first verify the emotional state at each filterbank and then the final recognition is obtained from a multi-decision method scheme. The database consists of 360 emotional utterances from twenty person who talk a sentence three times for six emotional states. The proposed method showed more 5% improvement of the recognition rate than previous works.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.385-392
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• 2021

The purpose of this study is to examine the effects of childhood trauma, rejection sensitivity, and emotional recognition clarity on displaced aggression An online survey was conducted with 208 adults at H cyber university. Correlation analysis and double mediation effect analysis were performed through SPSS Win 25 and SPSS Process Macro, and the results are as follows. First, childhood trauma and rejection sensitivity showed positive correlations with displaced aggression, and emotional recognition clarity showed negative correlations. Second, in the relationship between childhood trauma and displaced aggression, rejection sensitivity was found to indirectly mediate, but emotional recognition clarity did not. Third, in the relationship between childhood trauma and displaced aggression, rejection sensitivity and emotional recognition clarity were found to be double-mediated. These results are expected to be used as basic data to reduce the displaced aggression of those who have experienced childhood trauma.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.22-30
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• 2019

This study was to find out that the effects of emotional clarity and perspective-taking on communication of married persons targeting 206 peoples. It set the clarity of emotional recognition and perspective-taking as the independent variables and the communication as the dependent variable and verified them. The results revealed in this study are as follows. When looked at the correlation between the clarity of emotional recognition, perspective taking, and communication of married people, each variable showd close correlation. It showed that the communication of married people had a statistically significant effect to the clarity of emotional recognition and perspective taking through the multiple regression analysis. As above, the result of this study confirmed the clarity of emotional recognition and perspective taking as factors that affect to the communication. It confirmed that more positive and reasonable communication is possible when understand the emotion clearly and the perspective taking which is the ability of standing in other people's perspectives.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.78-91
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• 2022

The importance of emotional intelligence education in the development of children's augmented reality education content is overlooked. Therefore, in-depth research is needed to develop children's emotional intelligence. This study was conducted through theoretical consideration and case analysis. The proposal of this paper is that the augmented reality type for children aged 2 to 7 is suitable for indoor activities with marking recognition technology. To promote an understanding of emotions, a large screen is selected, and emoticon dolls or emoticon books are recommended for learning content. Children aged 7 to 11 are suitable for indoor activities of non-marker recognition technology, and can induce emotional control and emotional recognition through active manipulation. For the learning content, "3D art teaching content" and "Online Classic Musical" are recommended. Children after the age of 11 are suitable for non-marker recognition technology outdoor activities and improve each element of emotional intelligence through interaction with nature and society. For the learning content, 'Forest Play Activity through Art' and 'EQ Theater Play' are recommended. Through this paper, we intend to promote the development of children's augmented reality emotional intelligence education.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

• Journal of The Korean Society of Integrative Medicine
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• v.3 no.1
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• pp.63-69
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• 2015

Purpose: The purpose of this study was to investigate the differences in emotion recognition according to general characteristics of stroke patients. Method: The subjects consisted of 38 stroke patients receiving rehabilitation at S Hospital in Busan. Used the eMETT program to assess emotional cognition. Result: The age and duration of disease showed statistically significant differences in emotion recognition ability score, the gender and lesion showed a statistically significant difference in some emotion(p<.05). Conclusion: The results of this study it can be seen that the difference in emotion recognition ability in accordance with the general characteristics of the stroke. There will be a variety of future research related to standardized research or interventions targeted at stroke patients and normal controls to be carried out.