Aug 29, 2018 · We design a deep and shallow feature fusion convolutional network, which combines the feature from different levels of network for speech emotion recognition.
The proposed network allows us to fully exploit deep and shallow feature. The popular Berlin data set is used in our experiments, the experimental results show ...
Deep and shallow features fusion based on deep convolutional neural network for speech emotion recognition. Article. Full-text available. Dec 2018; Int J Speech ...
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In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract ...
A simple CNN (convolutional neural network) architecture, based on log-mel-spectrograms of segmented speech utterances, is proposed, used to extract the ...
This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach,
Deep and shallow features fusion based on deep convolutional neural network for speech emotion recognition. Recent years have witnessed the great progress ...
This paper performs speech emotion recognition on short voice messages lasting less than three seconds, using one-dimensional convolutional neural networks.
The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a ...
To learn Chaogram's high-level features and emotion classifications, the Visual Geometry Group (VGG) deep convolutional neural network (DCNN) is employed after ...