Chinese speech emotion recognition based on bidirectional long short-term memory network

Hu Du, Kaoru Hirota*, Yaping Dai, Donggyun Kim, Junjie Ma

*此作品的通讯作者

科研成果: 会议稿件论文同行评审

摘要

A Bidirectional Long Short-Term Memory(BLSTM) network is applied to improve the accuracy of Chinese speech emotion recognition of six basic human emotions (angry, fear, happy, neutral, sad, and surprise). The features of emotions can be learned and saved by BLSTM network whose special architecture called memory blocks is used to remember information from a long sentence, and BLSTM network provides information both from history and future of the current frame for the importance of the context of sentences. Results of experiments on the CASIA Chinese emotion corpus show that the average recognition accuracy reaches 73.83%, and has a 9.83% increase compared with the method based on information cell, 7.83% increase compared with Mel Frequency Cepstrum Coefficient and Principal Component Analysis, and 24.83% increase compared with Random Deep Belief Networks.

源语言英语
出版状态已出版 - 2017
活动5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017 - Beijing, 中国
期限: 2 11月 20175 11月 2017

会议

会议5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017
国家/地区中国
Beijing
时期2/11/175/11/17

指纹

探究 'Chinese speech emotion recognition based on bidirectional long short-term memory network' 的科研主题。它们共同构成独一无二的指纹。

引用此