Applying Machine Learning Techniques for Speech Emotion Recognition

被引:0
|
作者
Tarunika, K. [1 ]
Pradeeba, R. B. [1 ]
Aruna, P. [2 ]
机构
[1] Coimbatore Inst Technol, MSC Software Syst, Coimbatore, Tamil Nadu, India
[2] Coimbatore Inst Technol, Dept Comp, Coimbatore, Tamil Nadu, India
关键词
K-nearest neighbor; Deep Neural Network; utterance level; Speech emotion recognition; artificial intelligence;
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
Emotion is an instinctive or intuitive feeling which is distinguished from reasoning or knowledge, it is a strong feeling derived from one's circumstance or surroundings. The main idea of the paper is to apply Deep Neural Network (DNN) and k-nearest neighbor (k-NN) in recognition of emotion from speech-especially scary state of mind. The area of application of the system is mainly concerned over the health care units. The foundation of this research has its main firm applications in palliative care. Under most precise outcome the alert signals are made through cloud. Many raw data are collected under special emphasis techniques. The acoustic voice signals are converted to wave form, uttereance level feature extraction emotion classification, existing database recognition, alert signal creation through cloud is the sequence of steps to be followed. The findings of the paper lays a fruitful contribution to palliative care system.
引用
下载
收藏
页数:5
相关论文
共 50 条
  • [31] Speech emotion recognition using multimodal feature fusion with machine learning approach
    Sandeep Kumar Panda
    Ajay Kumar Jena
    Mohit Ranjan Panda
    Susmita Panda
    Multimedia Tools and Applications, 2023, 82 : 42763 - 42781
  • [32] A FEATURE FUSION METHOD BASED ON EXTREME LEARNING MACHINE FOR SPEECH EMOTION RECOGNITION
    Guo, Lili
    Wang, Longbiao
    Dang, Jianwu
    Zhang, Linjuan
    Guan, Haotian
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 2666 - 2670
  • [33] IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients
    Olatinwo, Damilola D.
    Abu-Mahfouz, Adnan
    Hancke, Gerhard
    Myburgh, Hermanus
    SENSORS, 2023, 23 (06)
  • [34] Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
    Kim, Tae-Wan
    Kwak, Keun-Chang
    APPLIED SCIENCES-BASEL, 2024, 14 (04):
  • [35] Language dialect based speech emotion recognition through deep learning techniques
    Sukumar Rajendran
    Sandeep Kumar Mathivanan
    Prabhu Jayagopal
    Maheshwari Venkatasen
    Thanapal Pandi
    Manivannan Sorakaya Somanathan
    Muthamilselvan Thangaval
    Prasanna Mani
    International Journal of Speech Technology, 2021, 24 : 625 - 635
  • [36] Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine
    Han, Kun
    Yu, Dong
    Tashev, Ivan
    15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4, 2014, : 223 - 227
  • [37] Language dialect based speech emotion recognition through deep learning techniques
    Rajendran, Sukumar
    Mathivanan, Sandeep Kumar
    Jayagopal, Prabhu
    Venkatasen, Maheshwari
    Pandi, Thanapal
    Sorakaya Somanathan, Manivannan
    Thangaval, Muthamilselvan
    Mani, Prasanna
    INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2021, 24 (03) : 625 - 635
  • [38] Analysis of Windowing Techniques for Speech Emotion Recognition
    Pereira, Mildred
    Chapaneri, Santosh
    Jayaswal, Deepak
    2016 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES), 2016,
  • [39] Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest
    Mashhadi, Mohammad Mahdi Rezapour
    Osei-Bonsu, Kofi
    PLOS ONE, 2023, 18 (11):
  • [40] Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools
    Fayan R.
    Montajabi Z.
    Gonsalves R.
    SMPTE Motion Imaging Journal, 2024, 133 (02): : 48 - 57