• 한국음향학회지
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• 제28권5호
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• pp.477-482
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• 2009

고객센터 (call-center)와 같은 기업환경의 감성인식 시스템은 감성 훈련용 음성과 불특정 고객들의 질의 음성간의 녹취 환경차이로 인해 상당한 시스템 성능 저하와 불안정성을 겪게 된다. 본 논문에서는 이러한 문제점을 극복하기 위해 기존의 전통적인 평상/화남 감성 분류체계를 남 녀 성별에 따른 감성별 특성 변화를 적용하여 2단계 분류체계로 확장하였다. 실험 결과, 제안한 방법은 녹취 환경 차이로 인한 시스템 불안정성을 해소할 수 있을 뿐 아니라 약 25% 가까운 인식 성능 개선을 가져올 수 있었다.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.611-614
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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 is expressed in the speech, the gesture, the physiological phenomena(the breath, the beating of the pulse, etc). In this paper, the emotion recognition method model using neuro-fuzzy in order to have cognizance of emotion from voice signal is presented and simulated.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2463-2466
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• 2003

This paper studied the pattern recognition algorithm and feature parameters for emotion recognition. In this paper, KNN algorithm was used as the pattern matching technique for comparison, and also VQ and GMM were used lot speaker and context independent recognition. The speech parameters used as the feature are pitch, energy, MFCC and their first and second derivatives. Experimental results showed that emotion recognizer using MFCC and their derivatives as a feature showed better performance than that using the Pitch and energy Parameters. For pattern recognition algorithm, GMM based emotion recognizer was superior to KNN and VQ based recognizer

• 한국지능시스템학회논문지
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• 제16권6호
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• pp.647-652
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• 2006

본 논문은 일반적으로 많은 특징들을 갖고 있는 패턴 분류 문제인 감정 인식을 위한 새로운 특징 선택 방법을 제안한다. '특징 선택'은 패턴 인식 성능의 향상에 기여하고 '차원의 저주'문제에도 좋은 해결책으로 많이 사용되는 방법이다. 그래서, 본 논문에서는 강화학습의 개념을 사용한 상호 작용에 의한 특징 선택 방법인 IFS(Interactiv Feature Selection)를 고안하였고 이 알고리즘을 사용하여 선택된 특징들을 감정 인식 시스템에 적용하여 성능이 향상됨을 확인하였다. 또한 기존의 특징 선택 방법과의 비교를 통하여 본 알고리즘의 우수성을 확인하였다.

• 대한치매학회지
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• 제23권3호
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• pp.146-160
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• 2024

Background and Purpose: The emotions of people at various stages of dementia need to be effectively utilized for prevention, early intervention, and care planning. With technology available for understanding and addressing the emotional needs of people, this study aims to develop speech emotion recognition (SER) technology to classify emotions for people at high risk of dementia. Methods: Speech samples from people at high risk of dementia were categorized into distinct emotions via human auditory assessment, the outcomes of which were annotated for guided deep-learning method. The architecture incorporated convolutional neural network, long short-term memory, attention layers, and Wav2Vec2, a novel feature extractor to develop automated speech-emotion recognition. Results: Twenty-seven kinds of Emotions were found in the speech of the participants. These emotions were grouped into 6 detailed emotions: happiness, interest, sadness, frustration, anger, and neutrality, and further into 3 basic emotions: positive, negative, and neutral. To improve algorithmic performance, multiple learning approaches were applied using different data sources-voice and text-and varying the number of emotions. Ultimately, a 2-stage algorithm-initial text-based classification followed by voice-based analysis-achieved the highest accuracy, reaching 70%. Conclusions: The diverse emotions identified in this study were attributed to the characteristics of the participants and the method of data collection. The speech of people at high risk of dementia to companion robots also explains the relatively low performance of the SER algorithm. Accordingly, this study suggests the systematic and comprehensive construction of a dataset from people with dementia.

• 문화기술의 융합
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• 제10권1호
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• pp.617-623
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• 2024

언어와 감정 사이의 복잡한 관계의 특징을 보이며, 우리의 말을 통해 감정을 식별하는 것은 중요한 과제로 인식된다. 이 연구는 음성 및 텍스트 데이터를 모두 포함하는 다중 모드 분류 작업을 통해 음성 언어의 감정을 식별하기 위해 속성 엔지니어링을 사용하여 이러한 과제를 해결하는 것을 목표로 한다. CNN(Convolutional Neural Networks)과 LSTM(Long Short-Term Memory)이라는 두 가지 분류기를 BERT 기반 사전 훈련된 모델과 통합하여 평가하였다. 논문에서 평가는 다양한 실험 설정 전반에 걸쳐 다양한 성능 지표(정확도, F-점수, 정밀도 및 재현율)를 다룬다. 이번 연구 결과는 텍스트와 음성 데이터 모두에서 감정을 정확하게 식별하는 두 모델의 뛰어난 능력을 보인다.

• 대한전자공학회논문지SP
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• 제45권4호
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• pp.107-114
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• 2008

본 논문은 남/여 성별에 기반해 음성을 평상, 기쁨, 슬픔, 화남의 4가지 감성 상태로 분류하는 감성인식 시스템을 구축하였다. 제안된 시스템은 입력 음성으로부터 1차적으로 남/여 성별을 분류하고, 분류된 성별을 기반으로 남/여 각기 최적의 특징벡터 열을 적용하여 감성인식을 수행함으로써 감성인식 성공률을 향상시켰다. 또한 음성인식에서 주로 사용되는 ZCPA(Zero Crossings with Peak Amplitudes)를 감성인식용 특징벡터로 사용하여 성능을 향상시켰으며, 남/여 각각의 특징 벡터 열을 최적화하기 위해 SFS(Sequential Forward Selection) 기법을 사용하였다. 감성 패턴 분류기로는 k-NN과 SVM을 비교하여 실험하였다. 실험결과 제안 시스템은 4가지 감성상태에 대해 약 85.3%의 높은 감성 인식 성공률을 달성할 수 있어 향후 감성을 인식하는 콜센터, 휴머노이드형 로봇이나 유비쿼터스(Ubiquitous) 환경 등 다양한 분야에서 감성인식 정보를 유용하게 사용될 수 있을 것으로 기대된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.122-126
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• 2002

In this study, we find features of 3 emotions (Happiness, Angry, Surprise) as the fundamental research of emotion recognition. Speech signal with emotion has several elements. That is, voice quality, pitch, formant, speech speed, etc. Until now, most researchers have used the change of pitch or Short-time average power envelope or Mel based speech power coefficients. Of course, pitch is very efficient and informative feature. Thus we used it in this study. As pitch is very sensitive to a delicate emotion, it changes easily whenever a man is at different emotional state. Therefore, we can find the pitch is changed steeply or changed with gentle slope or not changed. And, this paper extracts formant features from speech signal with emotion. Each vowels show that each formant has similar position without big difference. Based on this fact, in the pleasure case, we extract features of laughter. And, with that, we separate laughing for easy work. Also, we find those far the angry and surprise.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(B)
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• pp.231-234
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• 2011

Audiovisual-based human emotion recognition can be considered a good approach for multimodal humancomputer interaction. However, the optimal multimodal information fusion remains challenges. In order to overcome the limitations and bring robustness to the interface, we propose a framework of automatic human emotion recognition system from speech and face display. In this paper, we develop a new approach for fusing information in model-level based on the relationship between speech and face expression to detect automatic temporal segments and perform multimodal information fusion.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.366-369
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• 2000

This paper examines three algorithms to recognize speaker's emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likeligood Bayes), NN(Nearest Neighbor) and HMM (Hidden Markov Model) algorithms are used as the pattern matching techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB and NN are composed of pitch mean, pitch standard deviation, energy mean, energy standard deviation, etc. For HMM, vectors of delta pitch with delta-delta pitch and delta energy with delta-delta energy are used. We recorded a corpus of emotional speech data and performed the subjective evaluation for the data. The subjective recognition result was 56% and was compared with the classifiers' recognition rates. MLB, NN, and HMM classifiers achieved recognition rates of 68.9%, 69.3% and 89.1% respectively, for the speaker dependent, and context-independent classification.