• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.459-480
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• 2010

A dialogue system includes various morphological analyses for recognizing a user's intention from the user's utterances. However, a user can represent various intentions via emotional states in addition to morphological expressions. Thus, a user's emotion recognition can analyze a user's intention in various manners. This paper presents a new method to automatically recognize a user's emotion for a dialogue system. For general emotions, we define nine categories using a psychological approach. For an optimal feature set, we organize a combination of sentential, a priori, and context features. Then, we employ a support vector machine (SVM) that has been widely used in various learning tasks to automatically classify a user's emotions. The experiment results show that our method has a 62.8% F-measure, 15% higher than the reference system.

• Proceedings of the KAIS Fall Conference
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• 2011.12a
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• pp.295-298
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• 2011

인간의 능력 중 가장 주목할 만한 것은 언어를 습득하고 그것을 이용하여 서로 의사소통을 할 수 있다는 것이다. 모든 언어에는 그 언어만이 가지는 특수성뿐만 아니라 공통적으로 존재하는 보편적인 특성이 있다. 이것 외에도 언어 위에 입혀지는 사람의 목소리는 의사소통을 하는데 있어 상대의 심리를 파악하는 중요한 단서가 된다. 특히, 언어는 습득되어져야 활용되고 그 습득되어지는 환경에 영향을 받으며 이러한 환경에 따라 사람의 목소리, 억양 등이 변화하게 되는 것이다. 따라서 본 논문에서는 음성신호 분석 기법을 적용하여 장르별 영화시청에 따른 시각적, 청각적 요인이 목소리에 미치는 영향을 분석하는 연구를 수행하였다. 이를 위해 장르별 영화를 시청한 후 성대 진동 및 음성에너지의 크기 변화를 측정하여 감정변화를 분석하는 실험을 수행하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.46-50
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• 2001

Voice is one of the most efficient communication media and it includes several kinds of factors about speaker, context emotion and so on. Human emotion is expressed in the speech, the gesture, the physiological phenomena (the breath, the beating of the pulse, etc). In this paper, the method to have cognizance of emotion from anyone's voice signals is presented and simulated by using neuro-fuzzy model.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.322-324
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• 2002

이동 통신 기술의 발달 및 대중화로 인하여 거의 모든 개인 사용자가 휴대폰을 사용하게 되었으며 휴대폰은 초기의 음성통화 기능 뿐만 아니라 VM(Virtual Machine)을 탑재하여 컴퓨터와 마찬가지로 프로그램을 작성 및 설치하여 사용할 수 있게 되었다. 본 논문은 휴대폰과 같은 특수한 임베디드 환경에서 감정 기반의 캐릭터 에이전트를 구현하기 위한 구조를 제안한다. 임베디드 기기의 특수성을 파악하고 주어진 제한사항을 극복하기 위해 기존의 단일 컴퓨터 상의 에이전트 아키텍처를 휴대폰과 서버의 역할을 분담하는 아키텍처로 변환하여 사용자로 하여금 컴퓨터와 근접한 수준의 캐릭터 에이전트를 이용할 수 있도록 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.445-446
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• 2019

본 논문에서는 단순히 필요한 정보를 얻기 위한 수준에 그쳤던 현대의 인공지능을 SER (Speech Emotion Recognition) 기술을 이용하여 사용자와 직접적으로 대화하는 형식으로 발전시키고자 한다. 사용자의 음성 언어에서 감정을 추출하여 인공지능 분야 및 챗봇과 대화함에 있어 좀더 효과적으로 해석할 수 있도록 도움을 준다. 이것을 대화형 시뮬레이션 게임에 접목시켜 단순한 선택형 대화 방식이 아닌 구어체로 대화하며 사용자에게 높은 몰입도를 줄 수 있다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.687-688
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• 2022

기존 언어치료는 언어발달장애 아동과 언어치료사간의 대면 방식으로 이루어지며, 이때 각종 언어치료도구가 사용된다. 본 시스템은 언어발달장애 아동과 로봇 간의 언어치료를 위해서, 센서를 통한 아동 이동 접근 확인, 아동 얼굴 검출, 아동 얼굴 인식, 아동 얼굴 감정 인식, 아동 얼굴 추적, 감정 돌봄 대화 출력, 언어치료 콘텐츠 표시, 음성대화를 통한 언어치료 수행이 어떻게 적용되는지 개념을 설명하고자 한다.

• Annual Conference of KIPS
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• 2023.05a
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• pp.411-412
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• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing SER, we can get numerous benefits. By using a convolutional neural network and Long Short Term Memory (LSTM ) method as a part of Artificial intelligence, the SER system can be built.

• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.247-255
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• 2015

This paper presents a study on the performance of the music search based on the automatically recognized music-emotion labels. As in the other media data, such as speech, image, and video, a song can evoke certain emotions to the listeners. When people look for songs to listen, the emotions, evoked by songs, could be important points to consider. However; very little study has been done on the performance of the music-emotion labels to the music search. In this paper, we utilize the three axes of human music perception (valence, activity, tension) and the five basic emotion labels (happiness, sadness, tenderness, anger, fear) in measuring music similarity for music search. Experiments were conducted on both genre and singer datasets. The search accuracy of the proposed emotion-based music search was up to 75 % of that of the conventional feature-based music search. By combining the proposed emotion-based method with the feature-based method, we achieved up to 14 % improvement of search accuracy.

• Journal of IKEEE
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• v.22 no.4
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• pp.1123-1130
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• 2018

A cat robot for the Autism Spectrum Disorders(ASD) treatment was designed and conducted field test. The designed robot had emotion expressing action through interaction by the touch, and performed a reasonable emotional expression based on Artificial Neural Network(ANN). However these operations were difficult to use in the various healing activities. In this paper, we describe a motion design that can be used in a variety of contexts and flexibly reaction with various kinds of situations. As a necessary element, the speech recognition system using the speech data collection method and ANN was suggested and the classification results were analyzed after experiment. This ANN will be improved through collecting various voice data to raise the accuracy in the future and checked the effectiveness through field test.

• Journal of Music and Human Behavior
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• v.13 no.2
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• pp.67-83
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• 2016

This study investigated the tonal characteristics in Korean emotion words by analyzing the pitch patterns transformed from word utterance. Participants were 30 women, ages 19-23. Each participant was instructed to talk about their emotional experiences using 4-syllable target words. A total of 180 utterances were analyzed in terms of the frequency of each syllable using the Praat. The data were transformed into meantones based on the semi-tone scale. When emotion words were used in the middle of a sentence, the pitch pattern was transformed to A3-A3-G3-G3 for '즐거워서(joyful)', C4-D4-B3-A3 for '행복해서(happy)', G3-A3-G3-G3 for '억울해서(resentful)', A3-A3-G3-A3 for '불안해서(anxious)', and C4-C4-A3-G3 for '침울해서(frustrated)'. When the emotion words were used at the end of a sentence, the pitch pattern was transformed to G4-G4-F4-F4 for '즐거워요(joyful)', D4-D4-A3-G3 for '행복해요(happy)', G3-G3-G3-A3 and F3-G3-E3-D3 for '억울해요(resentful)', A3-G3-F3-F3 for '불안해요(anxious)', and A3-A3-F3-F3 for '침울해요(frustrated)'. These results indicate the differences in pitch patterns depending on the conveyed emotions and the position of words in a sentence. This study presents the baseline data on the tonal characteristics of emotion words, thereby suggesting how pitch patterns could be utilized when creating a melody during songwriting for emotional expression.