… the emotion classes makes the classification task very difficult. This paper proposed a deep
learning technique in the context of emotional recognition, in order to classify emotion labels …

… neural networks for speech emotion recognition have been introduced. While one group of
these models designs the neural network … 1-D Convolutional Neural Network (CNN) model as …

… Given that the primary focus of this work is on vision based emotion recognition, we simply
used … It uses 1582 features extracted with the open-source Emotion and Affect Recognition (…

… Emotion recognition in … emotion recognition in speech is a significant and applicable research
topic, and present a system for emotion recognition using oneclass-in-one neural networks…

… recognizing emotions. In this paper, we present a new model for continuous emotion recognition
… -to-end, is comprised of a Convolutional Neural Network (CNN), which extracts features …

… [37] have achieved maximum accuracy up to 99.3% but at the cost of 22 layers neural
network. Training such a large network is a time-consuming job. Compared to existing methods, …

… determining emotional states. Inspired by this success, we propose an emotion recognition
… To this purpose, we utilize a convolutional neural network (CNN) to extract features from the …

… , which aims at recognizing emotions from complete utterances, … a Deep Neural Network
(DNN) that recognizes emotions from … Recognizing emotions from a one second spectrogram …

… important for emotion recognition. However, … network framework called spatial–temporal
RNN (STRNN) to deal with both EEG-based emotion recognition and facial emotion recognition. …

… approaches cannot encode and learn both semantic and emotional relationship in short
text … posed a novel neural network architecture, called semanticemotion neural network (SENN) …