  Detecting emotions directly from a speech signal plays an important role in
effective human-computer interactions. Existing speech emotion recognition
models require massive computational and storage resources, making them hard to
implement concurrently with other machine-interactive tasks in embedded
systems. In this paper, we propose an efficient and lightweight fully
convolutional neural network for speech emotion recognition in systems with
limited hardware resources. In the proposed FCNN model, various feature maps
are extracted via three parallel paths with different filter sizes. This helps
deep convolution blocks to extract high-level features, while ensuring
sufficient separability. The extracted features are used to classify the
emotion of the input speech segment. While our model has a smaller size than
that of the state-of-the-art models, it achieves higher performance on the
IEMOCAP and EMO-DB datasets.
