Databases, features and classifiers for speech emotion recognition: a review

被引:178
|
作者
Swain, Monorama [1 ]
Routray, Aurobinda [2 ]
Kabisatpathy, P. [3 ]
机构
[1] Silicon Inst Technol, Dept Elect & Commun Engn, Bhubaneswar, Odisha, India
[2] Indian Inst Technol Kharagpur, Elect Engn, Kharagpur, W Bengal, India
[3] CV Raman Coll Engn, Dept Elect & Commun, Bhubaneswar, Odisha, India
关键词
Speech corpus; Excitation features; Spectral features; Prosodic features; Classifiers; Emotion recognition;
D O I
10.1007/s10772-018-9491-z
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 is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.
引用
收藏
页码:93 / 120
页数:28
相关论文
共 50 条
  • [41] Analysis of Features and Classifiers in Emotion Recognition Systems: Case Study of Slavic Languages
    Nedeljkovic, Zeljko
    Milosevic, Milana
    Durovic, Zeljko
    ARCHIVES OF ACOUSTICS, 2020, 45 (01) : 129 - 140
  • [42] Survey on Emotion Recognition Databases
    Hong, Juyoung
    Hwang, Yujin
    Lee, Gwangjin
    Choi, Yukyung
    2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022), 2022, : 1173 - 1178
  • [43] EEG Databases for Emotion Recognition
    Liu, Yisi
    Sourina, Olga
    2013 INTERNATIONAL CONFERENCE ON CYBERWORLDS (CW), 2013, : 302 - 309
  • [44] Speech Based Emotion Recognition Using Spectral Feature Extraction and an Ensemble of kNN Classifiers
    Rieger, Steven A., Jr.
    Muraleedharan, Rajani
    Ramachandran, Ravi P.
    2014 9TH INTERNATIONAL SYMPOSIUM ON CHINESE SPOKEN LANGUAGE PROCESSING (ISCSLP), 2014, : 589 - +
  • [45] A systematic literature review of speech emotion recognition approaches
    Singh, Youddha Beer
    Goel, Shivani
    NEUROCOMPUTING, 2022, 492 : 245 - 263
  • [46] Speech Emotion Recognition
    Lalitha, S.
    Madhavan, Abhishek
    Bhushan, Bharath
    Saketh, Srinivas
    2014 INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS, COMPUTERS AND COMMUNICATIONS (ICAECC), 2014,
  • [47] Emotion Recognition with Boosted Tree Classifiers
    Day, Matthew
    ICMI'13: PROCEEDINGS OF THE 2013 ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 2013, : 531 - 534
  • [48] Comparison Of Different Classifiers for Emotion Recognition
    Iliou, Theodoros
    Anagnostopoulos, Christos-Nikolaos
    13TH PANHELLENIC CONFERENCE ON INFORMATICS, PROCEEDINGS, 2009, : 102 - 106
  • [49] Pattern recognition and features selection for speech emotion recognition model using deep learning
    Jermsittiparsert, Kittisak
    Abdurrahman, Abdurrahman
    Siriattakul, Parinya
    Sundeeva, Ludmila A.
    Hashim, Wahidah
    Rahim, Robbi
    Maseleno, Andino
    INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2020, 23 (04) : 799 - 806
  • [50] Pattern recognition and features selection for speech emotion recognition model using deep learning
    Kittisak Jermsittiparsert
    Abdurrahman Abdurrahman
    Parinya Siriattakul
    Ludmila A. Sundeeva
    Wahidah Hashim
    Robbi Rahim
    Andino Maseleno
    International Journal of Speech Technology, 2020, 23 : 799 - 806