This paper proposes an emotion recognition system using a deep learning approach from emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a speech signal is first processed in
For example, in a method based on coincidence filtering approach [19], automatic extraction and classification of features [20], or emotion recognition using a multi-column CNN model [21]. In order to improve model accuracy, many studies combined deep learning techniques to extract features and ...
Deep learning techniques have proven to be effective in solving the facial emotion recognition (FER) problem. However, it demands a significant amount of supervision data which is often unavailable due to privacy and ethical concerns. In this paper, we p
Moreover, this pre-processed database is converted into spectrograms so that it can be fed into deep-learning models. In addition, the model extracts the spectral features from the spectrogram that will be used for the training of the machine. Figure 3 Flowchart for Speech Emotion recognition ...
Recently, deep learning methodologies have become popular to analyse physiological signals in multiple modalities via hierarchical architectures for human emotion recognition. In most of the state-of-the-arts of human emotion recognition, deep learning for emotion classification was used. However, deep le...
Speech emotion recognition is a challenging problem partly because it is unclear what features are effective for the task. In this paper we propose to utilize deep neural networks (DNNs) to extract high level features from raw data and show that they are effective...
This research makes significant advances in the field of emotion recognition by presenting a new generative adversarial network (GAN) model that integrates deep learning with electroencephalography (EEG). To achieve more accurate data production and real data matching, the model utilizes self-attention...
This project aims to classify the emotion on a person's face into one ofseven categories, using deep convolutional neural networks. The model is trained on theFER-2013dataset which was published on International Conference on Machine Learning (ICML). This dataset consists of 35887 grayscale, 48...
Speech emotion recognition (SER) has many challenges and limitations in the literature that need to be solved by using an efficient approach. We explored various deep learning approaches for the SER tasks, conducted different experimentations, and proposed a new architecture for the SER, which utili...
[NLU] DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation,程序员大本营,技术文章内容聚合第一站。