• 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.

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 s.182
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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.

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.

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.

Emotion recognition from speech using Gammatone auditory filterbank

  • Le, Ba-Vui;Lee, Young-Koo;Lee, Sung-Young
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06a
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    • pp.255-258
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    • 2011
  • An application of Gammatone auditory filterbank for emotion recognition from speech is described in this paper. Gammatone filterbank is a bank of Gammatone filters which are used as a preprocessing stage before applying feature extraction methods to get the most relevant features for emotion recognition from speech. In the feature extraction step, the energy value of output signal of each filter is computed and combined with other of all filters to produce a feature vector for the learning step. A feature vector is estimated in a short time period of input speech signal to take the advantage of dependence on time domain. Finally, in the learning step, Hidden Markov Model (HMM) is used to create a model for each emotion class and recognize a particular input emotional speech. In the experiment, feature extraction based on Gammatone filterbank (GTF) shows the better outcomes in comparison with features based on Mel-Frequency Cepstral Coefficient (MFCC) which is a well-known feature extraction for speech recognition as well as emotion recognition from speech.

Emotion Recognition by Vision System (비젼에 의한 감성인식)

  • 이상윤;오재흥;주영훈;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2001.12a
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    • pp.203-207
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    • 2001
  • In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

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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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