  We present a Multi-Window Data Augmentation (MWA-SER) approach for speech
emotion recognition. MWA-SER is a unimodal approach that focuses on two key
concepts; designing the speech augmentation method and building the deep
learning model to recognize the underlying emotion of an audio signal. Our
proposed multi-window augmentation approach generates additional data samples
from the speech signal by employing multiple window sizes in the audio feature
extraction process. We show that our augmentation method, combined with a deep
learning model, improves speech emotion recognition performance. We evaluate
the performance of our approach on three benchmark datasets: IEMOCAP, SAVEE,
and RAVDESS. We show that the multi-window model improves the SER performance
and outperforms a single-window model. The notion of finding the best window
size is an essential step in audio feature extraction. We perform extensive
experimental evaluations to find the best window choice and explore the
windowing effect for SER analysis.
