  In Speech Emotion Recognition (SER), emotional characteristics often appear
in diverse forms of energy patterns in spectrograms. Typical attention neural
network classifiers of SER are usually optimized on a fixed attention
granularity. In this paper, we apply multiscale area attention in a deep
convolutional neural network to attend emotional characteristics with varied
granularities and therefore the classifier can benefit from an ensemble of
attentions with different scales. To deal with data sparsity, we conduct data
augmentation with vocal tract length perturbation (VTLP) to improve the
generalization capability of the classifier. Experiments are carried out on the
Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved
79.34% weighted accuracy (WA) and 77.54% unweighted accuracy (UA), which, to
the best of our knowledge, is the state of the art on this dataset.
