SPEECH EMOTION RECOGNITION USING RBF KERNEL OF LIBSVM

被引:0
|
作者
Chavhan, Y. D. [1 ]
Yelure, B. S. [1 ]
Tayade, K. N. [1 ]
机构
[1] GCE, Karad, Karad, India
关键词
Speech emotion; Emotion Recognition; LIBSVM; MFCC and MEDC; RBF;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The LIBSVM is used as classifier to identify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database. The results are taken by using RBF kernel of LIBSVM. It gives 93.75% recognition accuracy for RBF kernel.
引用
收藏
页码:1132 / 1135
页数:4
相关论文
共 50 条
  • [11] A Review on Emotion Recognition using Speech
    Basu, Saikat
    Chakraborty, Jaybrata
    Bag, Arnab
    Aftabuddin, Md.
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), 2017, : 109 - 114
  • [12] Speech Emotion Recognition Using CNN
    Huang, Zhengwei
    Dong, Ming
    Mao, Qirong
    Zhan, Yongzhao
    PROCEEDINGS OF THE 2014 ACM CONFERENCE ON MULTIMEDIA (MM'14), 2014, : 801 - 804
  • [13] Speech Emotion Recognition with MPCA and Kernel Partial Least Squares Regression
    Xin, Minghai
    Gu, Weiyi
    Wang, Jinlong
    JOURNAL OF COMPUTERS, 2014, 9 (04) : 998 - 1004
  • [14] Speech Emotion Recognition Based on Deep Learning and Kernel Nonlinear PSVM
    Han Zhiyan
    Wang Jian
    PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019, : 1426 - 1430
  • [15] Speech Emotion Recognition Based on Kernel Reduced-rank Regression
    Zheng, Wenming
    Zhou, Xiaoyan
    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), 2012, : 1972 - 1976
  • [16] Speech Emotion Recognition Based on Kernel Partial Least Squares Regression
    Gu, Weiyi
    2009 THE REGIONAL WORKSHOP OF THE INTERNATIONAL SOCIETY FOR THE STUDY OF BEHAVIOURAL DEVELOPMENT (ISSBD): SOCIAL AND EMOTIONAL DEVELOPMENT IN CHANGING SOCIETIES, 2009, : 93 - 96
  • [17] Speech emotion recognition using emotion perception spectral feature
    Jiang, Lin
    Tan, Ping
    Yang, Junfeng
    Liu, Xingbao
    Wang, Chao
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2021, 33 (11):
  • [18] Relative Entropy Normalized Gaussian Supervector for Speech Emotion Recognition using Kernel Extreme Learning Machine
    Li, Ruru
    Yang, Dali
    Li, Xinxing
    Wang, Renyu
    Xu, Mingxing
    Zheng, Thomas Fang
    2016 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA), 2016,
  • [19] USING REGIONAL SALIENCY FOR SPEECH EMOTION RECOGNITION
    Aldeneh, Zakaria
    Provost, Emily Mower
    2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 2741 - 2745
  • [20] Emotion Recognition in Speech Using Neural Networks
    J. Nicholson
    K. Takahashi
    R. Nakatsu
    Neural Computing & Applications, 2000, 9 : 290 - 296