• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.471-480
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• 2007

Negative emotions play an important function as to human's existence. In this research, we employed the audio-visual film clips to induce negative emotions and examined the classified responses in the autonomic nervous system(ANS) due to each negative emotion.30 adults(22.6 years $old{\pm}1.24$, 15 males and 15 females) took part in this experiment. Through the preliminary experiment, 2 minutes film's stimuli were selected as the emotion-induced stimuli. During the period when participants were viewing and listening to the selected movie, EDA and ECG were examined as soon as one stimulus was displayed, participants were tested by completing the psychological appraisals of their experienced emotion due to each emotional stimulus. With regard to the result of analyzing the psychological responses, each negative emotion appropriately and effectively induced its target emotion. While concerning the result of analyzing ANS responses, each negative emotion induced its respective activation in ANS. What is more, compared with other types of negative emotional stimuli, the scaring stimulus induced higher activation of the sympathetic nervour system(SNS) as to the indexes in EDh and ECG. This research made segmentation of ANS responses to each negative emotion, which has its significance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.409-414
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• 2023

We described how to implement character expressions and animations that play an important role in expressing emotions and personalities in low-polygon character animation. With the development of the video industry, character expressions and mouth-shaped lip-syncing in animation can realize natural movements at a level close to real life. However, for non-experts, it is difficult to use expert-level advanced technology. Therefore, We aimed to present a guide for low-budget low-polygon character animators or non-experts to create mouth-shaped lip-syncing more naturally using accessible and highly usable features. A total of 8 mouth shapes were developed for mouth shape lip-sync animation: 'ㅏ', 'ㅔ', 'ㅣ', 'ㅗ', 'ㅜ', 'ㅡ', 'ㅓ' and a mouth shape that expresses a labial consonant. In the case of facial expression animation, a total of nine animations were produced by adding highly utilized interest, boredom, and pain to the six basic human emotions classified by Paul Ekman: surprise, fear, disgust, anger, happiness, and sadness. This study is meaningful in that it makes it easy to produce natural animation using the features built into the modeling program without using complex technologies or programs.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• The Journal of the Convergence on Culture Technology
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• v.2 no.4
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• pp.31-40
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• 2016

The hardware of the human body is given the life force by the sentence which is the physiological software that the program for cell activation by the electrical signal enters. The aim of this study is to create a better therapeutic environment for the human body that groaned with errors in the physiological and cognitive systems that are transmitted to neurons and neurons. The sentence program of the rated sijo, which is the software of the human body which has the function as a conductor to connect the emotions of joy, anger, sadness, and enthusiasm to the human mental system, can be connected to the neuron system of the human body, we tried to identify the principle of operating the motherboard of mind in humanities. Once these principles are identified, we can figure out how to minimize side effects and lead the body to a therapeutic program. The research found that there is a strong energy source that can operate the motherboard of the heart very quickly in the rated Sijo. This is because it is confirmed that new coding and re-coding of a number of rated sijo, or a new syllable of one syllable followed by the original syllable of the original syllable, are formed quickly and therapeutically.This has led to the possibility of literary therapy for mankind to upgrade the human psychic system in abundance through the function of the interaction between the sentence as a conductor that is synaptically connected to the human body and the mainboard of the mind attached to the human body without side effects in the future.

• Phonetics and Speech Sciences
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• v.14 no.3
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• pp.77-86
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• 2022

In this study, we reported the validation results of the Korean Affective Voice Database (KAV DB), an affective voice database available for scientific and clinical use, comprising a total of 113 validated affective voice stimuli. The KAV DB includes audio-recordings of two actors (one male and one female), each uttering 10 semantically neutral sentences with the intention to convey six different affective states (happiness, anger, fear, sadness, surprise, and neutral). The database was organized into three separate voice stimulus sets in order to validate the KAV DB. Participants rated the stimuli on six rating scales corresponding to the six targeted affective states by using a 100 horizontal visual analog scale. The KAV DB showed high internal consistency for voice stimuli (Cronbach's α=.847). The database had high sensitivity (mean=82.8%) and specificity (mean=83.8%). The KAV DB is expected to be useful for both academic research and clinical purposes in the field of communication disorders. The KAV DB is available for download at https://kav-db.notion.site/KAV-DB-75 39a36abe2e414ebf4a50d80436b41a.

• Science of Emotion and Sensibility
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• v.20 no.4
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• pp.15-30
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• 2017

The purpose of this study was to verify the basic emotion theory based on the emotion-related research using functional brain imaging technology. For this purpose, a meta-analysis on the functional magnetic resonance imaging (fMRI) studies was performed. Six individual emotions-joy, happiness, fear, anger, disgust, sadness-were selected. In order to collect the fMRI data of individual emotions, we searched the electronic journals such as Medline, PsychInfo, PubMed for the past 10 years. fMRI experiment data aimed at healthy subjects for 6 emotions were collected, and only studies reported in Talairach or MNI standard coordinate system were included. In order to eliminate the difference between Talairach and MNI coordinate systems, we analyzed fMRI data based on the Talairach coordinate system. A meta-analysis using GingerALE 2.3 program adopting the activation likelihood estimates (ALE) techniques was performed. In this study, we confirmed that the individual emotions are associated with consistent and distinguishable regional brain responses within the framework of the basic emotion theory. The conclusion of this study of the brain areas associated with each individual emotional reaction was substantially consistent with the results of existing review articles. Finally, the limitations of this study and some suggestions for the future research were presented.

• Korean Journal of Forensic Psychology
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• v.11 no.3
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• pp.329-347
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• 2020

When a person suggests an estimate under uncertainty, (s)he tend to rely on the information and number provided in advance. As a result, their final estimate would be assimilated to the initial value. This phenomenon is called "anchoring effect". The present research examined anchoring effects observed in law courts. Sentencing decision of jurors can be influenced by the sentence demanded by the prosecutor. Specifically, this study demonstrated the condition in which anchoring effect would be stronger and practical solutions for lowering anchoring effect. Study 1 demonstrated whether gravity of criminal cases and levels of anchor influenced anchoring effects. As expected, anchoring effect was stronger in a heavier criminal case than in a lighter one. When a low anchor was provided in a lighter case, anchoring effect was stronger compared to when a high anchor was provided. Study 2 examined how emotion affects anchoring effects. The results showed that anchoring effect appeared to be significantly stronger with feelings of anger than of sadness. Study 3 examined the solution for reducing anchoring effects in a court. When activation of selective-accessibility model was prevented, anchoring effects significantly decreased. These results can help solve the problems about juror judgmental bias and contribute to the development of Korean jury trial.

• (The)Study of the Eastern Classic
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• no.66
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• pp.193-226
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• 2017

This writing establishes identity of JinSeBaek, a male protagonist from a classical novel Hajinyangmunrok, through examining his relationship with a female character named HaOkJu. The character JinSeBaek was analyzed with attachment theory. JinSeBaek is a type of character who openly expresses his emotions. The evidence that JinSaeBaek falls in love with HaOkJu at a first sight was mainly shown through his physical affection with her. After the breakup, JinSeBaek expresses sadness, depression, and anger, as well as sheds tears. Although JinSeBaek repeatedly breaks up and gets back together with HaOkJu, JinSaeBaek consistently shows his desire to be with HaOkJu. Expressing true emotions was not ideal characteristic for men in this era. JinSaeBaek develops attachment to HaOkJu after he loses his parents, whom he has previously developed attachment to, but repeated break ups with HaOkJu leads to separation anxiety. Although his separation anxiety is caused by HaOkJu, it is also HaOkJu who can resolve the anxiety. The fact that JinSaeBaek honestly expresses his emotions and develops abnormal attachment to woman makes JinSaeBaek unique from other male protagonists. A character like JinSaeBaek is not common in other fictional novels or romance novels as well. Thus, JinSaebaek is claimed to be extraordinary character in literature.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.