• ETRI Journal
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• v.40 no.5
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• pp.643-652
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• 2018

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature-extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well-known Mel-frequency cepstral coefficient features. For classification purposes, the use of a multi-layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text-dependent and text-independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.

• 한국HCI학회:학술대회논문집
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• 2007.02c
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• pp.322-325
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• 2007

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms. (c) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.500-519
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• 2019

The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to determine from a specific piece of content the overall attitude of its author in relation to a specific item, product, brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover and extract the subjective information from a given text, researchers have applied various methods in computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to facilitate future research in this field.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.77-99
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• 2002

This study compares avatar-based chatting and text-based chatting. The comparison focuses on the effect of different chatting methods on user perception such as flow, social presence, and media richness. Especially the effects of avatar are examined across varying task types-work-oriented and fun-oriented. To accomplish this objective, a laboratory experiment was conducted using 80 experienced subjects. The results indicate that avatar-based chatting was more playfulness than text-based chatting in general. However, the effects of chatting methods on user perception were quite different according to the task types. There was no significant difference between avatar-based chatting and text-based chatting in the fun-oriented task, but avatar-based chatting was perceived as a more playful, focused, telepresent, and social present method in the work-oriented task.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.1
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• pp.54-63
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• 1999

The Landscape drawing is used as main media in landscape design process like the language in daily life for human. Designers input many intentions and meaningful words in design process through landscape drawing. The common purpose of landscape drawing is to represent reality effectively, even though it has variable visual forms and materiality. The representation in landscape drawing in metaphorical as well as visual and functional. But current tendency is inclined to use landscape drawing in a functional aspect for visual representation and the landscape drawing is utilized straight-forwardly rather than metaphorically for clear communication. Such recognition on landscape drawing results from the difficulty to accept the symbolic aspect of the drawing. The difficulty makes the utilization and the interpretation of landscape drawing stay at conventional level in following visible factors. For the sake of solving the difficulty this study considers landscape drawing as the text that contains readable objects and symbolic words. This study presents layer-methods for reading a landscape drawing as a text; situational and contextural reading, iconological reading and reading the subject of drawing.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1437-1446
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• 2020

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids' book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids' book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.193-195
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• 2012

본 논문에서는 모바일 환경에서 이동중에 사용자에게 스포츠 경기를 관전할 수 있는 실시간 중계 시스템을 제안한다. 제안하는 시스템은 문자 중계를 기본으로 하여 소셜 미디어와 TV 중계 영상을 이용한 하이라이트 동영상 서비스와 소셜 미디어 요약 서비스를 함께 제공한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.173-183
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• 2019

This study aims to utilize big data contents of news and social media for developing a corporate strategy of multinational enterprises and their global decision-making through the data mining technique, especially text mining. In this paper, the data of 2 news media (BBC and CNN) and 2 social media (Facebook and Twitter) were collected for the three global leading Korean companies (Samsung, Hyundai Motor Company, and LG) from April, 2018 to April, 2019. The findings of this paper have shown that traditional news media and also modern social media have become devastating tools to extract global trends or phenomena for businesses. Moreover, this presents that a company can adopt a two-track strategy through two different types of media by deriving the key issues or trends from news media channels and also grasping consumers' sentiments, preference or issues of interest such as battery or design from social media. In addition, analyzing the texts of those media and understanding the association rules greatly contribute to the comparison between two different types of media channels to see the difference. Lastly, this provides meaningful and valuable data empowered insights to find a future direction comprehensively and develop a global strategy for sustainability of business.

• ETRI Journal
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• v.36 no.5
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• pp.704-713
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• 2014

Most natural language processing tasks depend on the outputs of some other tasks. Thus, they involve other tasks as subtasks. The main problem of this type of pipelined model is that the optimality of the subtasks that are trained with their own data is not guaranteed in the final target task, since the subtasks are not optimized with respect to the target task. As a solution to this problem, this paper proposes a consolidation of subtasks for a target task ($CST^2$). In $CST^2$, all parameters of a target task and its subtasks are optimized to fulfill the objective of the target task. $CST^2$ finds such optimized parameters through a backpropagation algorithm. In experiments in which text chunking is a target task and part-of-speech tagging is its subtask, $CST^2$ outperforms a traditional pipelined text chunker. The experimental results prove the effectiveness of optimizing subtasks with respect to the target task.