• The Journal of Industrial Distribution & Business
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• v.10 no.5
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• pp.59-66
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• 2019

Purpose - Many studies show that dissatisfied customers are silent rather than expressing complaints directly to firms. Although silent voices are pervasive in service failure, they have received little attention from researchers. Silence implies a multidimensional nature, not just the opposite of voice. This study focuses on two types of silent students in higher education: acquiescent silence and defensive silence. This study also proposes cognitive trust and affective trust as variables affecting student silence. The objective of this study is to analyse the effects of trust types on student silence and exit intention. Research design, data, and methodology - To test the proposed model, this study conducted a survey with undergraduate students who selected silence in a dissatisfied relationship with a professor. Respondents were asked to respond to the questionnaire, recalling the dissatisfaction at that time. A total of 300 students was surveyed from whom 275 completed questionnaires was obtained. The structural equation model analysis was used for the hypothesis test. Results - First, cognitive trust was negatively related to acquiescent and defensive silence. Second, affective trust was negatively related to acquiescent and defensive silence. Third, cognitive trust was negatively exit intention, but affective trust didn't significantly reduce exit intention. Forth, acquiescent silence was positively related to exit intention, but defensive silence didn't have a significant positive impact on exit intention. Thus, a key result of this analysis was that acquiescent silence enhances exit intention. Conclusions - The findings of the study provide a better understanding of the types of silence, and the role of trust, thus furthering the implication of student reactions to dissatisfaction. In particular, this study is meaningful in that it confirms the value of student silence in the context of complaint management. Acquiescent silence should be more importantly managed because it has stronger negative motive than defensive silence. Acquiescent silence is reduced through various channels(mail, telephone, counseling) that can express complaints. Cognitive trust and affective trust are a essential factors in reducing silence. Also, in explaining exit intention, cognitive trust plays a more important role than affective trust.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2E
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• pp.98-104
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• 2002

The present study describes a combination method to recognize the human affective states such as anger, happiness, sadness, or surprise. For this, we extracted emotional features from voice signals and facial expressions, and then trained them to recognize emotional states using hidden Markov model (HMM) and neural network (NN). For voices, we used prosodic parameters such as pitch signals, energy, and their derivatives, which were then trained by HMM for recognition. For facial expressions, on the other hands, we used feature parameters extracted from thermal and visible images, and these feature parameters were then trained by NN for recognition. The recognition rates for the combined parameters obtained from voice and facial expressions showed better performance than any of two isolated sets of parameters. The simulation results were also compared with human questionnaire results.

• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.129-138
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• 2022

In the context of the increasing applications of voice assistants in vehicles, we focused on the association between the visual appeal of the cars and the acoustic characteristics of the voice assistants. This study aimed to investigate the relationship between the visual appeal of the vehicle and the voice assistant based on their emotional characteristics. A total of 15 adjectives were used to assess the emotional characteristics of 12 types of cars and six types of voices. An online interview was carried out, instructing participants to match three adjectives with the presented car images or voices. This was followed with a brief interview to allow the participants to reflect on the adjective matches. Based on the assessments, we performed principal component analysis (PCA) to determine factors. We aimed to deploy the cars and voices and analyze the patterns of clustering. The PCA analysis revealed two factors profiled as "Light-Heavy" and "Comfortable-Radical." Both car and voice stimuli were deployed in a two-dimensional space showing the internal relationship within and between the two substances. Based on the coordination data, a hierarchical cluster grouped the 18 stimuli into four groups labeled as challenge, elegance, majesty, and vigor. This study identified two latent factors describing the emotional characteristics of both car images and voice types clustered into four groups based on their emotional characteristics. The coherent matches between car style and voice type are expected to address the design concept more successfully.

• MALSORI
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• no.35_36
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• pp.37-48
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• 1998

When we hear someone's voice, even without having met the person before, we usually make up a certain mental image of the person. This study aims at investigating the relationship between the voice and the image information carried within the voice. Does the mental picture created by the voice closely reflect the real image and if not, is it related with the real image at all\ulcorner To answer the first question, a perception experiment was carried out. Speech samples reading a short sentence from 8 males and 8 females were recorded and pictures of subjects were also taken. Ajou University students were asked to participate in the experiment to match the voice with the corresponding picture. Participants in the experiment correctly match 1 female voice and 4 male voices with their corresponding pictures. However, it is interesting to note that even in cases of mismatch, the results show that there is a very strong tendency. In other words, even though participants falsely match a certain voice with a certain picture, majority of them chose the same picture for the voice. It is the case for all mismatches. It seems that voice does give the listener a certain impression about physical characteristics even if it might not be always correct. By showing that there is a clear relationship between voice and image, this study provides a starting point for further research on voice characteristics: what characteristics of the voice carry the relevant information\ulcorner This kind of study will contribute toward the understanding of the affective domain of human voice and toward the speech technology.