Research and Implementation of Speech Emotion Recognition Based on CGRU Model

被引：2

作者：

Zheng Y. ^{[1
]}

Chen J.-N. ^{[1
]}

Wu F. ^{[1
]}

Fu B. ^{[1
]}

机构：

[1] School of Information Science & Engineering, Northeastern University, Shenyang

来源：

Dongbei Daxue Xuebao/Journal of Northeastern University | 2020年 / 41卷 / 12期

关键词：

CGRU model; Data augmentation; Mel-frequency cepstral coefficients; Random forest; Speech emotion recognition;

D O I：

10.12068/j.issn.1005-3026.2020.12.002

中图分类号：

学科分类号：

摘要：

Speech emotion recognition is a very important research direction in emotion computing and human-computer interaction. At present, deep neural network is widely used to extract emotional features of speech, but further research is needed on which neural network model to use and how to alleviate the problem of model overfitting. To solve these problems, a CGRU model was proposed, which combined one dimensional convolutional neural networks (CNN) and gated circulation unit (GRU). The low-order and high-order emotional features of speech were extracted from the MFCC features of the original speech signal, and the features were selected through random forest, which achieved 79%, 69% and 75% recognition accuracy respectively on three common emotional corpus: EMODB, SAVEE, RAVDESS. By using the data augmentation technique, the sample size was increased by adding gaussian noise and changing the speed, which further improved the identification accuracy. The availability of the model in the real world was verified through the online identification system. © 2020, Editorial Department of Journal of Northeastern University. All right reserved.

引用

页码：1680 / 1685

页数：5

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