• Title, Summary, Keyword: Korean Emotion Feature

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Discriminative Feature Vector Selection for Emotion Classification Based on Speech (음성신호기반의 감정분석을 위한 특징벡터 선택)

  • Choi, Ha-Na;Byun, Sung-Woo;Lee, Seok-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.9
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    • pp.1363-1368
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    • 2015
  • Recently, computer form were smaller than before because of computing technique's development and many wearable device are formed. So, computer's cognition of human emotion has importantly considered, thus researches on analyzing the state of emotion are increasing. Human voice includes many information of human emotion. This paper proposes a discriminative feature vector selection for emotion classification based on speech. For this, we extract some feature vectors like Pitch, MFCC, LPC, LPCC from voice signals are divided into four emotion parts on happy, normal, sad, angry and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification.

Interactive Feature selection Algorithm for Emotion recognition (감정 인식을 위한 Interactive Feature Selection(IFS) 알고리즘)

  • Yang, Hyun-Chang;Kim, Ho-Duck;Park, Chang-Hyun;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.6
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    • pp.647-652
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    • 2006
  • This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive Feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

Use of Word Clustering to Improve Emotion Recognition from Short Text

  • Yuan, Shuai;Huang, Huan;Wu, Linjing
    • Journal of Computing Science and Engineering
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    • v.10 no.4
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    • pp.103-110
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    • 2016
  • Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

Comparison of EEG Feature Vector for Emotion Classification according to Music Listening (음악에 따른 감정분류을 위한 EEG특징벡터 비교)

  • Lee, So-Min;Byun, Sung-Woo;Lee, Seok-Pil
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.5
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    • pp.696-702
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    • 2014
  • Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. A selection of feature vectors is very important for the performance of EEG pattern classifiers. This paper proposes a comparison of EEG feature vectors for emotion classification according to music listening. For this, we extract some feature vectors like DAMV, IAV, LPC, LPCC from EEG signals in each class related to music listening and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification according to music listening.

Noise Robust Emotion Recognition Feature : Frequency Range of Meaningful Signal (음성의 특정 주파수 범위를 이용한 잡음환경에서의 감정인식)

  • Kim Eun-Ho;Hyun Kyung-Hak;Kwak Yoon-Keun
    • Journal of the Korean Society for Precision Engineering
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    • v.23 no.5
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    • pp.68-76
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    • 2006
  • The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Hence this paper describes the realization of emotion recognition. For emotion recognition from voice, we propose a new feature called frequency range of meaningful signal. With this feature, we reached average recognition rate of 76% in speaker-dependent. From the experimental results, we confirm the usefulness of the proposed feature. We also define the noise environment and conduct the noise-environment test. In contrast to other features, the proposed feature is robust in a noise-environment.

Development of Interactive Feature Selection Algorithm(IFS) for Emotion Recognition

  • Yang, Hyun-Chang;Kim, Ho-Duck;Park, Chang-Hyun;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.4
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    • pp.282-287
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    • 2006
  • This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

Feature Vector Processing for Speech Emotion Recognition in Noisy Environments (잡음 환경에서의 음성 감정 인식을 위한 특징 벡터 처리)

  • Park, Jeong-Sik;Oh, Yung-Hwan
    • Phonetics and Speech Sciences
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    • v.2 no.1
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    • pp.77-85
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    • 2010
  • This paper proposes an efficient feature vector processing technique to guard the Speech Emotion Recognition (SER) system against a variety of noises. In the proposed approach, emotional feature vectors are extracted from speech processed by comb filtering. Then, these extracts are used in a robust model construction based on feature vector classification. We modify conventional comb filtering by using speech presence probability to minimize drawbacks due to incorrect pitch estimation under background noise conditions. The modified comb filtering can correctly enhance the harmonics, which is an important factor used in SER. Feature vector classification technique categorizes feature vectors into either discriminative vectors or non-discriminative vectors based on a log-likelihood criterion. This method can successfully select the discriminative vectors while preserving correct emotional characteristics. Thus, robust emotion models can be constructed by only using such discriminative vectors. On SER experiment using an emotional speech corpus contaminated by various noises, our approach exhibited superior performance to the baseline system.

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The Comparison of Speech Feature Parameters for Emotion Recognition (감정 인식을 위한 음성의 특징 파라메터 비교)

  • 김원구
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.470-473
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    • 2004
  • In this paper, the comparison of speech feature parameters for emotion recognition is studied for emotion recognition using speech signal. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy. MFCC parameters and their derivatives with or without cepstral mean subfraction are also used to evaluate the performance of the conventional pattern matching algorithms. Pitch and energy Parameters were used as a Prosodic information and MFCC Parameters were used as phonetic information. In this paper, In the Experiments, the vector quantization based emotion recognition system is used for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy parameters. The vector quantization based emotion recognizer achieved recognition rates of 73.3% for the speaker and context independent classification.

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Emotion Recognition of Facial Expression using the Hybrid Feature Extraction (혼합형 특징점 추출을 이용한 얼굴 표정의 감성 인식)

  • Byun, Kwang-Sub;Park, Chang-Hyun;Sim, Kwee-Bo
    • Proceedings of the KIEE Conference
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    • pp.132-134
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    • 2004
  • Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.

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