  Speech emotion recognition (SER) has been a popular research topic in
human-computer interaction (HCI). As edge devices are rapidly springing up,
applying SER to edge devices is promising for a huge number of HCI
applications. Although deep learning has been investigated to improve the
performance of SER by training complex models, the memory space and
computational capability of edge devices represents a constraint for embedding
deep learning models. We propose a neural structured learning (NSL) framework
through building synthesized graphs. An SER model is trained on a source
dataset and used to build graphs on a target dataset. A relatively lightweight
model is then trained with the speech samples and graphs together as the input.
Our experiments demonstrate that training a lightweight SER model on the target
dataset with speech samples and graphs can not only produce small SER models,
but also enhance the model performance compared to models with speech samples
only and those using classic transfer learning strategies.
