• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.321-332
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• 2021

This study developed an English conversation learning program using virtual reality(VR) and mobile devices. Participants learned and practiced English conversational patterns in immersive virtual reality and mobile conditions. In the program, participants learned and practiced nine conversational patterns with virtual characters in four steps. Language anxiety and conversational fluency were measured to examine the effects of this program. Language anxiety questionnaire was administered before and after the experiment. The results showed that language anxiety was significantly reduced after learning in both conditions, and the reduction waa significantly greater in the immersive condition. Conversational fluency was assessed based on the changes in the length, appropriateness, and accuracy of the responses before and after participants learned and practiced conversational episodes. The results showed that the length, appropriateness, and accuracy of the responses were improved in both conditions after learning. The response length was significantly longer in the immersive VR conditions. These results suggest that immersive VR can be an effective tool to enhance English conversational abilities.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.251-260
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• 2022

Despite the development of technology and the increase in the spread of smart speakers, user satisfaction keeps decreasing due to conversational errors. This study aims to examine the effect of the self-repair strategy on user experience in the context of conversational agents of smart speakers. Scenarios were designed based on error situations, and participants were divided into two groups by task criticality. The results revealed that the agent's self-repair strategy has a negative effect on trust and perceived ease of use compared with performance without error. It also influenced adoption intention through interaction with task criticality. This study is significant in that it empirically investigated the effects of the self-repair strategy and the user experience factors related to the actual acceptance of the self-repair strategy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.27-38
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• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.263-271
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• 2008

This paper presents a Korean conversational agent system in a mobile environment using natural language processing techniques. The aim of a conversational agent in mobile environment is to provide natural language interface and enable more natural interaction between a human and an agent. Constructing such an agent, it is required to develop various natural language understanding components and effective utterance generation methods. To understand spoken style utterance, we perform morphosyntactic analysis, shallow semantic analysis including modality classification and predicate argument structure analysis, and to generate a system utterance, we perform example based search which considers lexical similarity, syntactic similarity and semantic similarity.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.374-384
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• 2018

This study examined the effects of clarification training on conversational breakdowns and repair strategies of 3 school aged children with ASD. Clarification training provided to the participants during playing the barrier games. The results found that the occurrence of conversational breakdown was decreased in terms of overall and types. The occurrence and appropriateness rates of repair strategies were increased among all participants. Based on the results, 'clarification training' used in this study can be an effective method to train children with ASD for appropriate repair strategies in conversation.

• Speech Sciences
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• v.8 no.4
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• pp.289-305
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• 2001

The conjunctive marker '-ko' in Korean can be interpreted as meaning either conjunctive 'and' or ordering 'and then'. The interpretation of '-ko' is ambiguous in written texts but not in spoken texts. It is because the meaning of the utterance is determined by the combination of the text with its prosody. The two meanings of ' -ko' can be explained by the theory of implicature, which was introduced by Grice (1973, 1981). This paper examines the meaning of the marker '-ko' with respect to the relation between its meaning and prosody. The results of the experiments in this paper showed that the prosodic phrasing in Korean influences the interpretation of the marker '-ko'. When two constituents combined by '-ko' are realized in the same accentual phrase, the marker can be interpreted as meaning 'exactly be orderly'. This meaning can be classified as the Particularlized Conversational Implicature (PCl) in Gricean theory. In the other cases of phrasing, the marker '-ko' can mean either 'conjunctive' or 'be orderly' by the Generalized Conversational Implicature (GCI). The fact that phrasing determines the interpretations of the marker '-ko' can be seen as supporting the view that prosody interacts with various levels of linguistic phenomena from phonology to pragmatics.

• Journal of Audiology & Otology
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• v.23 no.4
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• pp.187-192
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• 2019

Background and Objectives: Clear speech is an effective communication strategy used in difficult listening situations that draws on techniques such as accurate articulation, a slow speech rate, and the inclusion of pauses. Although too slow speech and improperly amplified spectral information can deteriorate overall speech intelligibility, certain amplitude of increments of the mid-frequency bands (1 to 3 dB) and around 50% slower speech rates of clear speech, when compared to those in conversational speech, were reported as factors that can improve speech intelligibility positively. The purpose of this study was to identify whether amplitude increments of mid-frequency areas and slower speech rates were evident in Korean clear speech as they were in English clear speech. Subjects and Methods: To compare the acoustic characteristics of the two methods of speech production, the voices of 60 participants were recorded during conversational speech and then again during clear speech using a standardized sentence material. Results: The speech rate and longterm average speech spectrum (LTASS) were analyzed and compared. Speech rates for clear speech were slower than those for conversational speech. Increased amplitudes in the mid-frequency bands were evident for the LTASS of clear speech. Conclusions:The observed differences in the acoustic characteristics between the two types of speech production suggest that Korean clear speech can be an effective communication strategy to improve speech intelligibility.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.149-163
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• 2024

As conversational AI systems such as ChatGPT have become more advanced, researchers are exploring ways to use them in education. However, we need effective ways to evaluate these systems before allowing them to help teach students. This study proposes a detailed framework for testing conversational AI across three important criteria as follow. First, specialized benchmarks that measure skills include giving clear explanations, adapting to context during long dialogues, and maintaining a consistent teaching personality. Second, adaptive standards check whether the systems meet the ethical requirements of privacy, fairness, and transparency. These standards are regularly updated to match societal expectations. Lastly, evaluations were conducted from three perspectives: technical accuracy on test datasets, performance during simulations with groups of virtual students, and feedback from real students and teachers using the system. This framework provides a robust methodology for identifying strengths and weaknesses of conversational AI before its deployment in schools. It emphasizes assessments tailored to the critical qualities of dialogic intelligence, user-centric metrics capturing real-world impact, and ethical alignment through participatory design. Responsible innovation by AI assistants requires evidence that they can enhance accessible, engaging, and personalized education without disrupting teaching effectiveness or student agency.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.351-368
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• 2005

The manual construction of the knowledge-base takes much time and effort, and it is hard to adjust intelligence systems to dynamic and flexible environment. Thus mental development in those systems has been investigated in recent years. Autonomous mental development is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Learning conversation, a kind of mental development, is an important aspect of conversational agents. In this paper, we propose a learning conversation method for conversational agents which uses several promising techniques; speech-act templates and genetic programming. Knowledge acquisition of conversational agents is implemented by finite state machines and templates, and dynamic sentence generation is implemented by genetic programming Several illustrations and usability tests how the usefulness of the proposed method.

• Journal of the HCI Society of Korea
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• v.12 no.1
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• pp.15-23
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• 2017

Applying the concept and components of human nature to the conversational agent in the smart home context, this study investigated the effects of the level of anthropomorphism and interaction type on the emotional user experiences and future use intention. The results of experiment study showed that the high-low condition of anthropomorphism and the voice-chatting interaction type have impacts on the perceived closeness, likability, and future use intention. That is, people evaluate the conversational agent as more close, likable, and useful when they perceive more human nature components and when in the voice interaction mode. Psychological resistance was lower in the voice than in the chatting mode regardless of the level of anthropomorphism. The results also demonstrated an interaction effect of anthropomorphism and interaction type on the future use intention: the effect of anthropomorphism existed only in the voice interaction mode. It leads to the conclusion that a conversational agent with the voice recognition interface should be designed with the higher level of human nature components for the continuous use.