A Study on Speech Emotion Recognition Using a Deep Neural Network

被引：14
|
作者：
Lee, Kyong Hee [1 ]
Choi, Hyun Kyun [1 ]
Jang, Byung Tae [1 ]
Kim, Do Hyun [1 ]
机构：
[1] ETRI, Intelligent Robot Res Div, Daejeon, South Korea
来源：
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE | 2019年
关键词：
speech; emotion; deep neural network; recognition; preprocessing;
D O I：
10.1109/ictc46691.2019.8939830
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：
When using voice signals as input to a deep learning network, there may be myriad features depending on the method and purpose of extracting the voice signal features. Therefore, extraction of appropriate features should be conducted. In this study, verbal features necessary for speech emotion recognition (SER) and preprocessing features for a deep neural network are described in detail. We implemented various preprocessing methods using voice features. Also, a Keras-based deep neural network using Python libraries was implemented. With these features, we could obtain a test accuracy of 68.5 % using the deep neural network (DNN). As a result, we confirmed that the proposed DNN improved an accuracy by 30.1 % compared to a support vector machine (SVM).
引用
收藏
页码：1162 / 1165
页数：4
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