• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.94-104
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• 2022

As the audio content market grows due to the spread of the AI speaker market and the influence of connected cars, the demand for podcast service is increasing. Therefore, in this study, the behaviors of podcast users were identified and the user types were classified. In the background study, podcast usage motives and user types were studied, and they were referred to when making the questionnaire. In the survey, preferred audio content was identified according to the situation, and in the in-depth interview, the user type and insights were derived by identifying the audio service usage behavior. As a result of the survey, there was little difference between preferred content for single listening and multitasking, but the difference in preferred content according to time period was statistically significant. The three user types derived from the in-depth interview were divided into users who listen alone for the purpose of study, find and listen to useful information quickly while on the go, and multitask and listen to the light and comfortable contents. It is expected that the results of this study will be an important reference for designing an audio content platform to improve user experience.

• Korean journal of communication and information
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• v.80
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• pp.193-223
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• 2016

This study tried to examine the transfiguration process of cable broadcasting business in the early 1960s of Korea. Especially, on the basis of a critical mind of examining autonomy of cable broadcasting operator as agent, this paper examined not only the technical and financial business management, and the drawing up of a program, but also business plans of the National Cable Broadcasting Association in initial establishment time of 1960s.

• Information Systems Review
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• v.21 no.3
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• pp.111-129
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• 2019

Recently, artificial intelligence (AI)-enabled products and services such as smartphones, smart speakers, chatbots are being released due to advances in AI technology. Thus researchers making effort to reveal that consumers' intention to adopt AI-enabled products. Yet, little is known about the intended adoption of AI-enabled products. Because most of studies has been not consideredthe perceived utility value of consumers for each attribute by classified based on the characteristics of AI-enabled products. Therefore, the purpose of this study is to investigate the difference in importance between attributes that affect the intention to adopt of AI-enabled products. For this, first, identified and classified the attributes of AI-enabled products based on IS Success Model of DeLone and McLean. Second, measured the utility value of each attribute on the adoption of AI-enabled products through conjoint analysis. And we employed construal level theory to see whether there are differences in the relative importance of AI-enabled products attributes depending on the temporal distance. Third, we segmented the market based on the utility value of each respondent through cluster analysis and tried to understand the characteristics and needs of consumers in each segment market. We expect to provide theoretical implications for conceptually structured attributes and factors of AI-enabled products and practical implications for how development efforts of AI-enabled products are needed to reach consumers need for each segment.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.91-102
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• 2020

The Internet of Things (IoT) is characterized as the market where various companies compete for the same consumers. Thus, there are differences in functions and performance provided by the main business area and other characteristics of the service providers. This paper investigates whether satisfaction with the service provided depends on the characteristics of the operator by using sentiment analysis of comments. To achieve this goal, word importance analysis and sensitivity analysis are conducted on 34,310 reviews of 41 applications registered in the Google Play. The review analysis was conducted at various levels, including TD-IDF (Term frequency-inverse document frequency) value of keywords, service sectors, the origin of providers, and domestic/foreign providers. The results show that users' overall assessment of IoT services was found to be low, and smart homes received relatively high reviews compared to other services, and manufacturing-based and overseas providers received relatively higher evaluations than others.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.675-681
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• 2021

With the recent growth of the AI speaker market, the demand for speech synthesis technology that enables natural conversation with users is increasing. Therefore, there is a need for a multi-speaker speech synthesis system that can generate voices of various tones. In order to synthesize natural speech, it is required to train with a large-capacity. high-quality speech DB. However, it is very difficult in terms of recording time and cost to collect a high-quality, large-capacity speech database uttered by many speakers. Therefore, it is necessary to train the speech synthesis system using the speech DB of a very large number of speakers with a small amount of training data for each speaker, and a technique for naturally expressing the tone and rhyme of multiple speakers is required. In this paper, we propose a technology for constructing a speaker encoder by applying the deep learning-based x-vector technique used in speaker recognition technology, and synthesizing a new speaker's tone with a small amount of data through the speaker encoder. In the multi-speaker speech synthesis system, the module for synthesizing mel-spectrogram from input text is composed of Tacotron2, and the vocoder generating synthesized speech consists of WaveNet with mixture of logistic distributions applied. The x-vector extracted from the trained speaker embedding neural networks is added to Tacotron2 as an input to express the desired speaker's tone.