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학술대회 A Study on Speech Emotion Recognition Using a Deep Neural Network
Cited 17 time in scopus Download 22 time Share share facebook twitter linkedin kakaostory
이경희, 최현균, 장병태, 김도현
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1162-1165
19ZS1300, 주력 산업 고도화를 위한 지능형 상황인지 기반 기술 개발, 김도현
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).
KSP 제안 키워드
Deep learning network, Deep neural network(DNN), Signal features, Speech Emotion recognition, Support VectorMachine(SVM), Voice features, Voice signal, deep learning(DL), preprocessing methods