• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• Korean Journal of Human Ecology
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• v.15 no.1
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• pp.17-29
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• 2006

This study investigated the behavior problems of children, both institutionalized and home-reared, depending on their sex, age, and entry to institution. The subjects included 621 children (274 institutionalized, 347 home-reared; 298 boys and 323 girls; 209 preschoolers, 223 first to third graders, 189 fourth to sixth). The data collected were analyzed with t-test, 3-way ANOVA, Scheffe test, and Pearson's correlation. Children's emotionality had two sides: Positive (happiness) and negative (sadness, anger, and fear). Measures of behavior problems included internalization (anxiety, immaturity, withdrawal, physical symptom) and externalization (hyperactivity, aggression). The results of this study indicated that the institutionalized were higher in negative emotionality than their counterparts, whereas the home-reared higher in positive emotionality. The institutionalized children also had higher externalizing problems than the home-reared. Furthermore, children's negative emotionality was positively related to their internalizing and externalizing behavior problems, while their positive emotionality was negatively related to the internalizing behavior problems.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3498-3512
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• 2021

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik's eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.67-78
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• 2015

This study investigated whether people experience a correspondence between color hue-tone and the main characters' emotions in the 2012 British musical drama film, Les $Mis\grave{e}rables$ through three practical experiments. Six screen images, which represent the characters' different emotions (Parrot's six primary types including love, joy, surprise, anger, sadness, and fear) were selected. For each screen image, participants were asked to judge the degree of the character's dominant emotions evoked from 17 varied screen images, which consisted of original chromatic and achromatized images as well as 15 color-filtered images (5 hues X 3 tones of the IRI color system). These tasks revealed that a chromatic color scheme is more effective to deliver the characters' positive emotions (i.e. love and joy) than an achromatic one. In addition, they proved that the hue and tone dimensions partially influence the relationships between the character emotions and colors.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.1955-1960
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• 2014

Background: This study was performed to determine the experiences of nursing students in caring for paediatric cancer patients and their families. Materials and Methods: This qualitative survey was carried out with 14 students studying in the nursing department of the Faculty of Health Sciences of a university in Edirne, Turkey. Data were obtained through focus group interviews and evaluated based on a qualitative content analysis. Results: It was determined that students, for the most part, experienced problems related to communication, sadness, helplessness, fear, anxiety, resentment and anger. In addition, the students were affected most often by effects of the disease and invasive procedures on paediatric cancer patients and their families during the process of caring for them in the oncology clinic. Conclusions: It would be useful to inform nursing students, prior to clinical practice, about the special needs of paediatric cancer patients and families who stay in oncology clinics, and to follow up with appropriate guidance during the clinical practices.

• Korean Journal of Child Studies
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• v.23 no.2
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• pp.107-120
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• 2002

This study compared the social skills and affective perspective taking of institutionalized and home-reared children. The subjects were 59 institutionalized and 60 home-reared children in Seoul. Results showed that institutionalized children were lower in cooperation, self-assertion and self-control than home-reared children. Institutionalized children were lower in delight, sadness, fear, and anger than home-reared children. As skills in self-control increased, the level of affective perspective taking about fear increased.

• Korean Journal of Child Studies
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• v.23 no.2
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• pp.31-47
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• 2002

In this study, the perceptions and responses of 136 kindergarten children from middle SES families were recorded in one-to-one interviews about the cause, reasons for expression, and responses to negative emotions. Results showed that children perceived he causes of anger and sadness as 'interpersonal events' and they perceived he cause of fear to be 'fantasy/scary events'. The children tended not to express their negative emotions because they expected negative responses from their peers and mothers, but when they did, the expressed their negative emotions to their mothers rather than to peers. Children responded to the negative emotions of their peers with 'problem-solving focused strategies', but they responded to their mothers' negative emotions with passive strategies, such as 'emotion focused response' and 'avoidance'.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.991-999
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• 2004

Face robots capable of expressing their emotional status, can be adopted as an efficient tool for friendly communication between the human and the machine. In this paper, we present a face robot actuated with artificial muscle based on dielectric elastomer. By exploiting the properties of polymers, it is possible to actuate the covering skin, eyes as well as provide human-like expressivity without employing complicated mechanisms. The robot is driven by seven types of actuator modules such as eye, eyebrow, eyelid, brow, cheek, jaw and neck module corresponding to movements of facial muscles. Although they are only part of the whole set of facial motions, our approach is sufficient to generate six fundamental facial expressions such as surprise, fear, anger, disgust, sadness, and happiness. Each module communicates with the others via CAN communication protocol fur the desired emotional expressions, the facial motions are generated by combining the motions of each actuator module. A prototype of the robot has been developed and several experiments have been conducted to validate its feasibility.