• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.1297-1297
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• 2017

본 연구는 대화형 인공지능 스피커의 수요 증진을 위한 새로운 비즈니스 전략 제시를 위해 수행되었다. 이를 위해 해당 IoT기기 소비자의 사용의도에 영향을 미치는 요인들을 선정하여 집중적으로 고찰하고 각 요인 사이에 어떤 상관관계가 존재하는지 실증적으로 분석하고자 하였다. 사용 의도에 영향을 미치는 요인으로는 기기 사용에 대한 안전성이 확보 되었음을 의미하는 안전에 대한 신뢰도와 기술적 흐름에 따른 시대적 압박, 기기 사용으로부터 발생하는 쾌락적 동기와 실용적 동기, 기기의 혁신성으로부터 오는 차별적 동기를 고려하였다. 본 연구의 수행을 통해 모든 요인은 각각 소비자의 사용 의도에 긍정적인 영향을 미치며 특히 차별화 욕구와 쾌락적 동기, 실용적 동기 간에는 유의한 양의 상관관계가 있을 것으로 나타낼 것으로 기대된다. 더불어, 본 연구의 핵심 연구 요인인 시대적 압박 요인의 경우 안전에 대한 신뢰도와 사용 의도에 있어 정의 영향을 주지만 차별화 욕구와 실용적 동기와는 음의 상관관계를 보일 것으로 예상된다. 본 연구는 일반적인 연구 모델에서 취급되는 주요 요인들과 더불어 시대적 압박이라는 새로운 요인을 제시하고 그 영향력을 논증하여 융합형 기술에 기반한 새로운 비즈니스 전략을 제시할 수 있다는 점에서 가치를 갖는다

• Journal of the HCI Society of Korea
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• v.14 no.1
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• pp.15-22
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• 2019

The purpose of this study is to investigate the users' expectation and expectation gap about the attributes of smart speaker as an intelligent agent, ie autonomy, sociality, responsiveness, activeness, time continuity, goal orientation. To this end, semi-structured interviews were conducted for smart speaker users and analyzed based on ground theory. Result has shown that people have huge expectation gap about the sociality and human-likeness of smart speakers, due to limitations in technology. The responsiveness of smart speakers was found to have positive expectation gap. For the memory of time-sequential information, there was an ambivalent expectation gap depending on the degree of information sensitivity and presentation method. We also found that there was a low expectation level for autonomous aspects of smart speakers. In addition, proactive aspects were preferred only when appropriate for the context. This study presents implications for designing a way to interact with smart speakers and managing expectations.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.242-264
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• 2019

This study focuses on considering the factors affecting the user intention to adopt an intelligent service - A.I. speaker services. Currently there can be a considerable difference between the expectation and the realized diffusion of IT-based intelligent services. This study aims to find out this gap based on the idea of diver previous researches including TAM and UTAUT studies and to identify the direct and indirect effects of diverse factors such as security issues, perceived time pressure, service innovativeness, and the experience of these IT-based intelligent services. And this study considers the expected impact of perceived time pressure factor on the user acceptance of A.I. speaker services. In analysis results, not only the traditional factors such as the perceived usefulness and the hedonic/utilitarian motives but also the perceived time pressure, the perceived security issues, and the experience of the services should be considered as meaningful factors to affect the users adopting A.I. speaker services.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.59-70
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• 2018

With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.

• Annual Conference of KIPS
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• 2017.11a
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• pp.1212-1215
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• 2017

현재 출시되는 AI스피커들의 기능들을 재현하면서 문제점을 찾아서 보완하고 특히 우리나라 1인 가구의 급격한 증가로 인한 다양한 사회 문제들의 해소 방안으로 표정인식을 통해 먼저 사용자에게 다가가는 감정적인 대화가 가능한 인공지능 서비스와 인터넷 환경에 무관한 홈 IoT 제어 그리고 시각데이터 제공이 가능한 다중 AI 스피커를 제작 하였다.

• Information Systems Review
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• v.22 no.2
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• pp.77-99
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• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.23-30
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• 2024

In this paper, we propose a deep learning-based personalized senior care service application. The proposed application uses Speech to Text technology to convert the user's speech into text and uses it as input to Autogen, an interactive multi-agent large-scale language model developed by Microsoft, for user convenience. Autogen uses data from previous conversations between the senior and ChatBot to understand the other user's intent and respond to the response, and then uses a back-end agent to create a wish list, a shared calendar, and a greeting message with the other user's voice through a deep learning model for voice cloning. Additionally, the application can perform home IoT services with SKT's AI speaker (NUGU). The proposed application is expected to contribute to future AI-based senior care technology.

• Annual Conference of KIPS
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• 2019.10a
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• pp.1151-1152
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• 2019

발달 장애는 신체 및 정신이 해당하는 나이에 맞게 발달하지 않은 상태로, 다른 아동에 비해 신경정신과적 질환 발생 확률이 높기 때문에 발달장애 아동의 치료는 매우 중요하다. 그러나 주관적 판단에 의해 이루어지는 기존 작업치료의 경우, 정량적 성과 지표를 확인하기 힘들고 대상자 스스로 지속적으로 진행하기에 한계가 있다. 본 연구에서는 치료 모델을 가상 공간상에 구현하여 공간에 구애받지 않고 치료를 진행할 수 있으며, 수행 결과에 대한 자료를 정확하고 지속적으로 기록하며 확인할 수 있도록 하였다. 또한, AI 스피커를 통해 치료에 대한 피드백을 줌으로써, 대상자 스스로 실시하여 치료자의 개입을 줄여 심리적 부담을 덜어 더욱 정확한 수행이 이루어지도록 하였다.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.