  Emotion recognition from speech signal based on deep learning is an active
research area. Convolutional neural networks (CNNs) may be the dominant method
in this area. In this paper, we implement two neural architectures to address
this problem. The first architecture is an attention-based CNN-LSTM-DNN model.
In this novel architecture, the convolutional layers extract salient features
and the bi-directional long short-term memory (BLSTM) layers handle the
sequential phenomena of the speech signal. This is followed by an attention
layer, which extracts a summary vector that is fed to the fully connected dense
layer (DNN), which finally connects to a softmax output layer. The second
architecture is based on a deep CNN model. The results on an Arabic speech
emotion recognition task show that our innovative approach can lead to
significant improvements (2.2% absolute improvements) over a strong deep CNN
baseline system. On the other hand, the deep CNN models are significantly
faster than the attention based CNN-LSTM-DNN models in training and
classification.
