End-to-End Speech Emotion Recognition Based on Neural Network

被引：0
|
作者：
Zhu, Bing [1 ]
Zhou, Wenkai [1 ]
Wang, Yutian [1 ]
Wang, Hui [1 ]
Cai, Juan Juan [1 ]
机构：
[1] Commun Univ China, Minist Educ, Key Lab Media Audio & Video, Beijing, Peoples R China
来源：
2017 17TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
speech emotion recognition; neural networks; end-to-end; FEATURES;
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：
Speech has lots of emotional information and speech emotional recognition is one of the top topic in AI field. Traditional research extracted speech feature first and then to classified the emotions by sorting algorithms. However, all kinds of feature extracting algorithms lost some original speech information to some extent, so the accuracy rate is reduced. In this paper, we present an end-to-end speech emotion recognition system which based on neural network without feature extraction. Experimental testing of the proposed scheme was performed using the DMO-DB (German) and CASIA (Chinese) emotional speech datasets, recognition rates reached 74.12% and 44.5% respectively.
引用
收藏
页码：1634 / 1638
页数：5
相关论文
共 50 条
  • [1] End-to-End Speech Emotion Recognition Based on One-Dimensional Convolutional Neural Network
    Gao, Mengna
    Dong, Jing
    Zhou, Dongsheng
    Zhang, Qiang
    Yang, Deyun
    [J]. 3RD INTERNATIONAL CONFERENCE ON INNOVATION IN ARTIFICIAL INTELLIGENCE (ICIAI 2019), 2019, : 78 - 82
  • [2] END-TO-END SPEECH EMOTION RECOGNITION USING DEEP NEURAL NETWORKS
    Tzirakis, Panagiotis
    Zhang, Jiehao
    Schuller, Bjoern W.
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 5089 - 5093
  • [3] A NOVEL END-TO-END SPEECH EMOTION RECOGNITION NETWORK WITH STACKED TRANSFORMER LAYERS
    Wang, Xianfeng
    Wang, Min
    Qi, Wenbo
    Su, Wanqi
    Wang, Xiangqian
    Zhou, Huan
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 6289 - 6293
  • [4] End-to-End Speech Emotion Recognition With Gender Information
    Sun, Ting-Wei
    [J]. IEEE ACCESS, 2020, 8 (08): : 152423 - 152438
  • [5] Unidirectional Neural Network Architectures for End-to-End Automatic Speech Recognition
    Moritz, Niko
    Hori, Takaaki
    Le Roux, Jonathan
    [J]. INTERSPEECH 2019, 2019, : 76 - 80
  • [6] End-to-end Triplet Loss based Emotion Embedding System for Speech Emotion Recognition
    Kumar, Puneet
    Jain, Sidharth
    Raman, Balasubramanian
    Roy, Partha Pratim
    Iwamura, Masakazu
    [J]. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 8766 - 8773
  • [7] End-To-End Speech Emotion Recognition Based on Time and Frequency Information Using Deep Neural Networks
    Bakhshi, Ali
    Wong, Aaron S. W.
    Chalup, Stephan
    [J]. ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, 325 : 969 - 975
  • [8] End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network
    Tang, Duowei
    Kuppens, Peter
    Geurts, Luc
    van Waterschoot, Toon
    [J]. EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 2021, 2021 (01)
  • [9] End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network
    Duowei Tang
    Peter Kuppens
    Luc Geurts
    Toon van Waterschoot
    [J]. EURASIP Journal on Audio, Speech, and Music Processing, 2021
  • [10] END-TO-END NEURAL NETWORK BASED AUTOMATED SPEECH SCORING
    Chen, Lei
    Tao, Jidong
    Ghaffarzadegan, Shabnam
    Qian, Yao
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 6234 - 6238
12345