• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1105-1110
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• 2017

Speech with various emotions degrades the performance of the speaker recognition system. In this paper, a speaker recognition method using emotional adaptation has been proposed to improve the performance of speaker recognition system using affective speech. For emotional adaptation, emotional speaker model was generated from speaker model without emotion using a small number of training affective speech and speaker adaptation method. Since it is not easy to obtain a sufficient affective speech for training from a speaker, it is very practical to use a small number of affective speeches in a real situation. The proposed method was evaluated using a Korean database containing four emotions. Experimental results show that the proposed method has better performance than conventional methods in speaker verification and speaker recognition.

• Science of Emotion and Sensibility
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• v.14 no.3
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• pp.371-384
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• 2011

Three experiments were carried out to examine how LED lighting hues influence to the rating and recognition of affective stimuli. In Experiment 1 and 2, IAPS affective pictures were used and an affective rating(valence and arousal) task and a recognition memory task were conducted under red, green, blue, and white hue LED lightings in Experiment 1 and cyan, magenta, yellow, and white ones in Experiment 2, respectively. In Experiment 3, affective words were used and the same two tasks were conducted under red, green, blue, and white hue LED lightings. According to the results of affective rating tasks, when primary hues(RGB) were used, red LED lighting elicited an excitement at the arousal dimension and green LED lighting evoked pleasantness at the valence one. When secondary hues(CMY) were used, magenta and cyan showed the similar but weaker patterns of responses comparing to red and green. The results of recognition memory task showed that the responses to the picture stimuli presented at green and cyan hue lightings tended to be a bit faster comparing to the stimuli presented at the other conditions but the difference was insignificant. In Experiment 3, however, recognition memory responses to the affective words presented at green hue lighting were faster significantly. These results indicate that warm colors like red and magenta elicit unpleasantness or excitement while cool colors like green and cyan evoke pleasantness or relaxation, and the primary hues provoke more positive or negative affectivity than secondary ones do. Particularly, the result of recognition memory task in Experiment 3 suggests that green hue LED lighting might be advantageous at the memory performance of language stimuli rather than visual ones.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.103-110
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• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.83-94
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• 2011

Main researching issue in affective computing is to give a machine the ability to recognize the emotion of a person and to react it properly. Efforts in that direction have mainly focused on facial and oral cues to get emotions. Postures have been recently considered as well. This paper aims to discriminate emotions posture by identifying and measuring the saliency of posture features that play a role in affective expression. To do so, affective postures from human subjects are first collected using a motion capture system, then emotional features in posture are described with spatial ones. Through standard statistical techniques, we verified that there is a statistically significant correlation between the emotion intended by the acting subjects, and the emotion perceived by the observers. Discriminant Analysis are used to build affective posture predictive models and to measure the saliency of the proposed set of posture features in discriminating between 6 basic emotional states. The evaluation of proposed features and models are performed using a correlation between actor-observer's postures set. Quantitative experimental results show that proposed set of features discriminates well between emotions, and also that built predictive models perform well.

• Korean Journal of Child Studies
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• v.6 no.2
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• pp.59-71
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• 1985

The purpose of this study was to analyze the effects of both social-status and mode of stimulus-presentation on children's empathic behavior, where empathy is defined as either the comprehension and recognition of an affective state in stimulus persons (others) or the empathic response to that perception. Middle-and lower-class kindergarten children were presented with a series of either short-sentence stories or short-pictorial stories. The subjects were asked to indicate how the child in each situation felt by selecting a "happy", "sad", or "angry" face to complete the picture accompanying each story. Immediately following the first question, children were asked to state verbally how the child in the picture might feel. The main results were (1) The mean empathy scores for the comprehension and recognition of an affective state in others and the empathic response to that perception was higher in middle-class children than in lower-class children. (2) There were differences in empathy scores to the three affective situations, i.e. the mean score for both happiness and sadness was significantly greater than for aggression, and (3) The empathy scores in the pictorial-stories were greater than in the sentence-stories.

• Journal of the Korean School Mathematics Society
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• v.19 no.1
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• pp.83-102
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• 2016

In this study we explored the effect of reading in mathematics classroom on five mathematical affective characteristics of middle school students. 100 2nd male middle schoolers' were participated in this study and five affective characteristics - interests, self-confidence, recognition of mathematics value, self-regulation, and mathematics anxiety- were investigated. According to the results, reading in mathematics class had an overall positive effect. Especially the characteristics interests and self-confidence of students' were improved. And for the low level students all characteristics were improved. And based on the result of pre and post test, and interview with 6 students, we suggest that desirable reading in mathematics classroom.

• The Korean Journal of Franchise Management
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• v.12 no.3
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• pp.47-58
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• 2021

Purpose: In general, franchise business models can generate higher returns and profits than non-franchise businesses. Therefore, it is necessary to study customer-based foodservice franchise brand awareness and service quality. The purpose of this study is to investigate the effect of service quality and brand awareness of foodservice franchises on attitudes divided into cognitive and affective attitudes and revisit intentions. Through this study, we intend to establish a structure that leads to service quality and brand awareness-cognitive attitude and affective attitude-loyalty. Research design, data, and methodology: In order to verify the hypothesis of this study, the survey was conducted among general consumers over the age of 20 who had visited a foodservice franchise within the last 3 months. Among the collected questionnaires, one insincere questionnaire was excluded, and 299 copies were used for analysis. The data collected to verify the hypothesis of this study were analyzed using SPSS 24.0 and AMOS 24.0. Result: First, it was found that the service quality of the foodservice franchise had a positive (+) effect on the cognitive attitude, and the service quality of the foodservice franchise had a statistically significant positive effect on the affective attitude. Second, the brand awareness of the foodservice franchise was found to have a statistically significant positive (+) effect on the cognitive attitude. and the brand awareness of the foodservice franchise had a statistically significant positive (+) effect on the affective attitude as well. Third, cognitive attitude was found to have a statistically significant positive (+) effect on loyalty, and affective attitude was also found to have a statistically significant positive (+) effect on loyalty. Conclusions: First, this study applied the S-O-R theory to the effect of service quality and brand recognition on cognitive attitude, affective attitude, and loyalty. Second, the structure leading to service quality and brand awareness-cognitive attitude and affective attitude-revisit intention was established. Third, attitudes in this study were divided into cognitive attitudes and affective attitudes. In general, attitude is studied as a single dimension as a cognitive attitude, but in this study, attitude was studied by dividing it into a cognitive dimension and an affective dimension

• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.207-215
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• 2005

Objectives:This study was to compare verbal memory ability among patients with schizophrenia, bipolar manic patients and unipolar depressive patients, and to understand their charicteristics of memory process. Methods:All subjects were hospitalized patients and had been interviewed by using the Structured Clinical Interview for DSM-IV(SCID). Schizophrenic patients(N=40), bipolar manic patients(N=17), and unipolar depressive patients(N=20) were assessed with K-AVLT for verbal memory and with K-WAIS for verbal IQ. Three groups were compared regarding total immediate recall, delayed recall, delayed recognition, learning curve, memory retention, and retrieval efficiency under controlled verbal IQ. Multiple regression analysis was performed to find which clinical factors have an influence on verbal memory ability. Results:In MANCOVA, differences of verbal memory test scores among the groups were statistically significant(F=1.800, p<.05). In post hoc analysis, Patients with schizophrenia and bipolar mania showed poorer performance in immediate recall, delayed recall, delayed recognition, retrieval efficiency than unipolar depres- sive patients. And schizophrenics performed poorly in delayed recall, delayed recognition, retrieval efficiency than nonpsychotic affective disorder group, but no difference in total immediate recall, delayed recall, delayed recognition, retrieval efficiency between the schizophrenic group and the psychotic affective group. Conclusions:These results partially confirm previous reports of verbal memory ability among major psychiatric disorders. Our results showed that psychotic symptoms were related with verbal memory, and longer duration of illness was related with poorer performance in schizophrenia and unipolar depression.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.603-623
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• 2023

While prior work has conducted individuals' password security behavior, there is a relatively neglect to examine individuals' affect and feelings of password fatigue in password change context. Therefore, this study explicated individuals' affective response to the feelings of password fatigue by drawing on several theoretical lens. Survey data collected from 267 users were used to test the model using partial least square analysis. This study found that feelings of password fatigue positively affected the negative password fatigue-induced affect, and also both the feelings of password fatigue and the negative password fatigue-induced affect were negatively related to attitude toward changing passwords, which in turn, leads to the intention to change passwords. Furthermore, this study found that shadow work recognition negatively moderated the relationship between attitude and behavioral intention. This study could offer a new theoretical perspective to understand an individual's security behavior and provide empirical evidences for practitioners in charge of IT security in organizations.