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권오욱,Ramu Venkatesan,도문호,지은희,조동운,이기원,김선여 한국식품과학회 2015 Food Science and Biotechnology Vol.24 No.2
This study evaluated the protective effects of dietary supplementation with a fermented barley and soybean mixture (BS) on ultraviolet (UV) B-induced photoaging in hairless mice. Skin aging-related parameters and protein levels related to skin wrinkles and moisturization in mice were analyzed. The BS reduced wrinkle formation, skin thickening, transepidermal water loss, and matrix metalloproteinases- 1 expression in skin. Skin hydration and pH were increased in the BS group. BS attenuated filaggrin expression as well as free amino acid and glycerol production. BS increased superoxide dismutase activity as well as increased expression of nuclear factor (erythroidderived 2)-like 2, procollagen type-I, and decreased erythema. These results suggest that BS protects against photoaging induced by UVB in vivo, indicating the potential of such mixtures as anti-photoaging dietary supplementation.
권오욱,홍택규,황금하,노윤형,최승권,김화연,김영길,이윤근,Kwon, O.W.,Hong, T.G.,Huang, J.X.,Roh, Y.H.,Choi, S.K.,Kim, H.Y.,Kim, Y.K.,Lee, Y.K. 한국전자통신연구원 2019 전자통신동향분석 Vol.34 No.4
In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.
권오욱,이기영,이요한,노윤형,조민수,황금하,임수종,최승권,김영길,Kwon, O.W.,Lee, K.Y.,Lee, Y.H.,Roh, Y.H.,Cho, M.S.,Huang, J.X.,Lim, S.J.,Choi, S.K.,Kim, Y.K. 한국전자통신연구원 2021 전자통신동향분석 Vol.36 No.1
In this study, we introduce trends in and the future of digital personal assistants. Recently, digital personal assistants have begun to handle many tasks like humans by communicating with users in human language on smart devices such as smart phones, smart speakers, and smart cars. Their capabilities range from simple voice commands and chitchat to complex tasks such as device control, reservation, ordering, and scheduling. The digital personal assistants of the future will certainly speak like a person, have a person-like personality, see, hear, and analyze situations like a person, and become more human. Dialogue processing technology that makes them more human-like has developed into an end-to-end learning model based on deep neural networks in recent years. In addition, language models pre-trained from a large corpus make dialogue processing more natural and better understood. Advances in artificial intelligence such as dialogue processing technology will enable digital personal assistants to serve with more familiar and better performance in various areas.
최대 사후 추정 화자 적응을 이용한 가변어휘 고립단어 음성인식기의 사무실 환경에서의 성능 평가
권오욱 한국음향학회 1998 韓國音響學會誌 Vol.17 No.2
본 논문에서는 임의의 단어를 인식하기 위하여 음성학적으로 최적화된 (phonetically-optimized word) 음성 데이터베이스를 사용하여 훈련된 가변어휘 고립단위 음 성인식기의 실제 인식기 사용 환경에서의 성능을 평가하였다. 이를 위하여, 훈련 데이터베이 스에서와 상이한 환경에서 수집된 음성학적으로 균형 잡힌(phonetically-balanced word) 고 립 단어 음성을 테스트 데이터로 사용하였다. 테스트 데이터는 일반적인 사무실에서 작동하 는 노트북 PC에서 내장 마이크를 사용하여 녹음되었다. 이렇게 녹음된 음성을 사용하여 고 립단어 인식기의 인식률을 측정하였다. 이 인식기는 최대 사후(maximum a posteriori) 추정 알고리듬을 사용하여 화자의 변화에 적응하였다. 컴퓨터 모의실험 결과에 의하면 화자 적응 을 하지 않은 기본 시스템은 깨끗한 음성에 대하여 81.3%에서 사무실 환경 음성에 대하여 69.8%로 인식률이 저하되었다. 사무실 환경 음성에 대하여, 비교사 점진(unsupervised incremental) 모드에서 최대 사후 추정 화자 적응 알고리듬을 적용하였을 경우에는 화자적 응을 하지 않은 경우에 비하여 9%의 에러를 감소시키며, 50단어의 적응 단어를 사용하여 교사 묶음(supervised batch) 모드에서 최대 사후 추정 화자 적응 알고리듬을 적용하였을 경우에는 16%의 에러를 감소시켰다.
권오욱,김회린,유창동,김봉완,이용주,Kwon Oh-Wook,Kim Hoi-Rin,Yoo Changdong,Kim Bong-Wan,Lee Yong-Ju 대한음성학회 2004 말소리 Vol.51 No.-
For educational and research purposes, a Korean speech recognition platform is designed. It is based on an object-oriented architecture and can be easily modified so that researchers can readily evaluate the performance of a recognition algorithm of interest. This platform will save development time for many who are interested in speech recognition. The platform includes the following modules: Noise reduction, end-point detection, met-frequency cepstral coefficient (MFCC) and perceptually linear prediction (PLP)-based feature extraction, hidden Markov model (HMM)-based acoustic modeling, n-gram language modeling, n-best search, and Korean language processing. The decoder of the platform can handle both lexical search trees for large vocabulary speech recognition and finite-state networks for small-to-medium vocabulary speech recognition. It performs word-dependent n-best search algorithm with a bigram language model in the first forward search stage and then extracts a word lattice and restores each lattice path with a trigram language model in the second stage.