  In this paper, we propose a new methodology for emotional speech recognition
using visual deep neural network models. We employ the transfer learning
capabilities of the pre-trained computer vision deep models to have a mandate
for the emotion recognition in speech task. In order to achieve that, we
propose to use a composite set of acoustic features and a procedure to convert
them into images. Besides, we present a training paradigm for these models
taking into consideration the different characteristics between acoustic-based
images and regular ones. In our experiments, we use the pre-trained VGG-16
model and test the overall methodology on the Berlin EMO-DB dataset for
speaker-independent emotion recognition. We evaluate the proposed model on the
full list of the seven emotions and the results set a new state-of-the-art.
