• Title, Summary, Keyword: emotion recognition

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Emotion Recognition Method Based on Multimodal Sensor Fusion Algorithm

  • Moon, Byung-Hyun;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.2
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    • pp.105-110
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    • 2008
  • Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

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.

Emotion Perception and Multisensory Integration in Autism Spectrum Disorder: A Review of Behavioral and Cognitive Neuroscience Studies (자폐 스펙트럼 장애의 다중감각 통합과 정서인식: 행동연구와 인지 신경 과학 연구에 대한 개관)

  • Cho, Hee-Joung;Kim, So-Yeon
    • Science of Emotion and Sensibility
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    • v.21 no.4
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    • pp.77-90
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    • 2018
  • Behavioral studies of emotion recognition in autism spectrum disorders (ASD) have yielded mixed results. Most of the studies focused on emotion recognition abilities with regard to ASD using stimuli with unisensory modality, making it difficult to determine difficulties in real life emotion perception in ASD. Herein, we review the recent behavioral and cognitive neuroscience studies on emotion recognition functions in ASD, including both unisensory and multisensory emotional information, to elucidate the possible difficulties in emotion recognition in ASD. In our study, we discovered that people with ASD have problems in the process of integrating emotional information during emotion recognition tasks. The following four points are discussed: (1) The restrictions of previous studies, (2) deficits in emotion recognition in ASD especially in recognizing multisensory information, (3) possible compensation mechanisms for emotion recognition in ASD, and (4) the possible roles of attention and language functions in emotion recognition in ASD. The compensatory mechanisms proposed herein for ASD with regard to emotion recognition abilities could contribute to a therapeutic approach for improving emotion recognition functions in ASD.

A study on the enhancement of emotion recognition through facial expression detection in user's tendency (사용자의 성향 기반의 얼굴 표정을 통한 감정 인식률 향상을 위한 연구)

  • Lee, Jong-Sik;Shin, Dong-Hee
    • Science of Emotion and Sensibility
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    • v.17 no.1
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    • pp.53-62
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    • 2014
  • Despite the huge potential of the practical application of emotion recognition technologies, the enhancement of the technologies still remains a challenge mainly due to the difficulty of recognizing emotion. Although not perfect, human emotions can be recognized through human images and sounds. Emotion recognition technologies have been researched by extensive studies that include image-based recognition studies, sound-based studies, and both image and sound-based studies. Studies on emotion recognition through facial expression detection are especially effective as emotions are primarily expressed in human face. However, differences in user environment and their familiarity with the technologies may cause significant disparities and errors. In order to enhance the accuracy of real-time emotion recognition, it is crucial to note a mechanism of understanding and analyzing users' personality traits that contribute to the improvement of emotion recognition. This study focuses on analyzing users' personality traits and its application in the emotion recognition system to reduce errors in emotion recognition through facial expression detection and improve the accuracy of the results. In particular, the study offers a practical solution to users with subtle facial expressions or low degree of emotion expression by providing an enhanced emotion recognition function.

Emotion Recognition using Robust Speech Recognition System (강인한 음성 인식 시스템을 사용한 감정 인식)

  • Kim, Weon-Goo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.5
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    • pp.586-591
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    • 2008
  • This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

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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Improved Two-Phase Framework for Facial Emotion Recognition

  • Yoon, Hyunjin;Park, Sangwook;Lee, Yongkwi;Han, Mikyong;Jang, Jong-Hyun
    • ETRI Journal
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    • v.37 no.6
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    • pp.1199-1210
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    • 2015
  • Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

The Pattern Recognition Methods for Emotion Recognition with Speech Signal (음성신호를 이용한 감성인식에서의 패턴인식 방법)

  • Park Chang-Hyeon;Sim Gwi-Bo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.347-350
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

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The Pattern Recognition Methods for Emotion Recognition with Speech Signal (음성신호를 이용한 감성인식에서의 패턴인식 방법)

  • Park Chang-Hyun;Sim Kwee-Bo
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.3
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    • pp.284-288
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

Pattern Recognition Methods for Emotion Recognition with speech signal

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.2
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    • pp.150-154
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.