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1D-CNN:Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

Computers, Materials & Continua 2021年第6期67卷 4039-4059页

作者： Mustaqeem Soonil Kwon Interaction Technology Laboratory Department of SoftwareSejong UniversitySeoul05006Korea

Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications,such as robotics,virtual reality,behavior assessments,and emergency call ***,researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches,but the recognition rate is still not *** main aim is to develop a new technique that increases the recognition rate with reasonable cost *** this paper,we suggested a new technique,which is a one-dimensional dilated convolutional neural network(1D-DCNN)for speech emotion recognition(SER)that utilizes the hierarchical features learning blocks(HFLBs)with a bi-directional gated recurrent unit(BiGRU).We designed a one-dimensional CNN network to enhance the speech signals,which uses a spectral analysis,and to extract the hidden patterns from the speech signals that are fed into a stacked one-dimensional dilated network that are called *** HFLB contains one dilated convolution layer(DCL),one batch normalization(BN),and one leaky_relu(Relu)layer in order to extract the emotional features using a hieratical correlation ***,the learned emotional features are feed into a BiGRU in order to adjust the global weights and to recognize the temporal *** final state of the deep BiGRU is passed from a softmax classifier in order to produce the probabilities of the *** proposed model was evaluated over three benchmarked datasets that included the IEMOCAP,EMO-DB,and RAVDESS,which achieved 72.75%,91.14%,and 78.01%accuracy,respectively.

关键词： Affective computing one-dimensional dilated convolutional neural network emotion recognition gated recurrent unit raw audio clips

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