  Speech Emotion Recognition (SER) is a fundamental task to predict the emotion
label from speech data. Recent works mostly focus on using convolutional neural
networks~(CNNs) to learn local attention map on fixed-scale feature
representation by viewing time-varied spectral features as images. However,
rich emotional feature at different scales and important global information are
not able to be well captured due to the limits of existing CNNs for SER. In
this paper, we propose a novel GLobal-Aware Multi-scale (GLAM) neural network
(The code is available at https://github.com/lixiangucas01/GLAM) to learn
multi-scale feature representation with global-aware fusion module to attend
emotional information. Specifically, GLAM iteratively utilizes multiple
convolutional kernels with different scales to learn multiple feature
representation. Then, instead of using attention-based methods, a simple but
effective global-aware fusion module is applied to grab most important
emotional information globally. Experiments on the benchmark corpus IEMOCAP
over four emotions demonstrates the superiority of our proposed model with 2.5%
to 4.5% improvements on four common metrics compared to previous
state-of-the-art approaches.
