首页    期刊浏览 2024年12月12日 星期四
登录注册

文章基本信息

  • 标题:Performance Evaluation of Deep Autoencoder Network for Speech Emotion Recognition
  • 本地全文:下载
  • 作者:Maria AndleebSiddiqui ; Wajahat Hussain ; Syed Abbas Ali
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2020
  • 卷号:11
  • 期号:2
  • DOI:10.14569/IJACSA.2020.0110276
  • 出版社:Science and Information Society (SAI)
  • 摘要:The learning methods with multiple levels of representation is called deep learning methods. The composition of simple but now linear modules results in deep-learning model. Deep-learning in near future will have many more success, because it requires very little engineering in hands and it can easily take ample amount of data for computation. In this paper the deep learning network is used to recognize speech emotions. The deep Autoencoder is constructed to learn the speech emotions (Angry, Happy, Neutral, and Sad) of Normal and Autistic Children. Experimental results evident that the categorical classification accuracy of speech is 46.5% and 33.3% for Normal and Autistic children speech respectively. Whereas, Auto encoder shows a very low classification accuracy of 26.1% for only happy emotion and no classification accuracy for Angry, Neutral and Sad emotions.
  • 关键词:Auto-encoder; emotions; DNN; classification accuracy; autism
国家哲学社会科学文献中心版权所有