  Speech emotion recognition (SER) is to study the formation and change of
speaker's emotional state from the speech signal perspective, so as to make the
interaction between human and computer more intelligent. SER is a challenging
task that has encountered the problem of less training data and low prediction
accuracy. Here we propose a data augmentation algorithm based on the imaging
principle of the retina and convex lens, to acquire the different sizes of
spectrogram and increase the amount of training data by changing the distance
between the spectrogram and the convex lens. Meanwhile, with the help of deep
learning to get the high-level features, we propose the Deep Retinal
Convolution Neural Networks (DRCNNs) for SER and achieve the average accuracy
over 99%. The experimental results indicate that DRCNNs outperforms the
previous studies in terms of both the number of emotions and the accuracy of
recognition. Predictably, our results will dramatically improve human-computer
interaction.
