http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
김경열 ( Kyungy-yeul Kim ),양영보 ( Cheol-soo Kim ),김미라 ( Mi-ra Kim ),박지수 ( Ji Su Park ),김지희 ( Jihie Kim ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
청소년의 성격유형을 분석할 때 소셜미디어 데이터를 활용하여 텍스트 처리로 분석하는 연구는 많이 알려져 있다. 그러나 이미지를 사용하여 성격유형을 분석한 연구는 미비하다. 본 연구는 청소년의 발테그 그림검사로 표현된 이미지를 데이터로 사용하고, CNN을 활용하여 MMTIC의 16가지 청소년의 성격유형을 예측한다. 연구 대상은 중학교 재학생을 대상으로 한다. MMTIC에서 U-band를 제외한 340명의 학생으로 2012년 4월부터 2013년 3월까지 조사하였다. 연구 결과 CNN을 사용하였을 때 21.6% 예측율을 보였으며, CNN Ensemble을 적용하였을 때 23.1%로 2.5%가 증가한다.
한국어 노래 음성 합성을 위한 웹 서비스 개발 및 연구
박지은(Jieun Park),김지희(Jihie Kim) 한국정보기술학회 2022 한국정보기술학회논문지 Vol.20 No.1
Recently, video content production has become more active, and there have been many demands for voice synthesis, such as cover song production, as well as dubbing and narration, which explain subtitles of video with desired voices. In this paper, a platform that allows users to experience various types of services in various voice options is presented. In the Proof of Concept that we developed, the Text To Speech was produced using Generative Flow for Text-to-Speech (Glow-TTS) and Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis (HIFI-GAN) and the Singing Voice Synthesis was generated with a parallel Korean singing voice synthesis system (MLP-Singer) and HIFI-GAN. Frontend, backend, and web server were built on the docker container. The service is provided as a website, and when the server gets a request, the voice synthesis result can be printed and used through voice file download. In this study, the overall system structure and implementation method for producing a voice synthesis platform were presented, and a method for additional supplementation and service was provided.
상대방 화자의 상태 문맥을 파악해 대화에서 화자의 감정을 인식하는 RNN 모델
임승욱(Seunguook Lim),김지희(Jihie Kim) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.7
Emotion recognition has increasingly received much attention in artificial intelligence, lately. In this paper, we present an RNN model that analyzes and identifies a speaker’s emotion appeared through utterances in conversation. There are two kinds of speaker considered context, self-dependency and inter-speaker dependency. In particular, we focus more on inter-speaker dependency by considering that the state context information of the relative speaker can affect the emotions of the current speaker. We propose a DialogueRNN based model that adds a new GRU Cell for catching inter-speaker dependency. Our model shows higher performance than the performances of DialogueRNN and its three variants on multiple emotion classification datasets.
일반 상식 기반 기계 독해를 위한 Type-specific 다중 헤드 공유 인코더 모델
채진영(Jinyeong Chae),김지희(Jihie Kim) Korean Institute of Information Scientists and Eng 2023 정보과학회논문지 Vol.50 No.5
Machine reading comprehension (MRC) is a task introduced to a machine that can understand natural languages by solving various tasks based on given context. To evaluate natural language understanding of machine, a machine must make commonsense inference under full comprehension of a given context. To enhance model obtaining such abilities, we proposed a multi-task learning scheme and a model for commonsense MRC. Contributions of this study are as follows: 1) a method of task-specific dataset configuration is proposed; 2) a type-specific multi-head shared-encoder model with multi-task learning scheme including batch sampling and loss scaling is developed; and 3) when the method is evaluated on CosmosQA dataset (commonsense MRC), the accuracy was improved by 2.38% compared to the performance at baseline with fine-tuning.