Speech emotion recognition with ensemble learning methods

Po Yuan Shih, Chia Ping Chen, Chung Hsien Wu

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose to apply ensemble learning methods on neural networks to improve the performance of speech emotion recognition tasks. The basic idea is to first divide unbalanced data set into balanced subsets and then combine the predictions of the models trained on these subsets. Several methods regarding the decomposition of data and the exploitation of model predictions are investigated in this study. On the public-domain FAU-Aibo database, which is used in Interspeech Emotion Challenge evaluation, the best performance we achieve is an unweighted average (UA) recall rate of 45.5% for the 5-class classification task. Furthermore, such performance is achieved with a feature space of 40-dimension. Compared to the baseline system with 384-dimension feature vector per example and an UA of 38.9%, such a performance is very impressive. Indeed, this is one of the best performances on FAU-Aibo within the static modeling framework.

原文English
主出版物標題2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2756-2760
頁數5
ISBN(電子)9781509041176
DOIs
出版狀態Published - 2017 6月 16
事件2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
持續時間: 2017 3月 52017 3月 9

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
國家/地區United States
城市New Orleans
期間17-03-0517-03-09

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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