  Speaker recognition systems are widely used in various applications to
identify a person by their voice; however, the high degree of variability in
speech signals makes this a challenging task. Dealing with emotional variations
is very difficult because emotions alter the voice characteristics of a person;
thus, the acoustic features differ from those used to train models in a neutral
environment. Therefore, speaker recognition models trained on neutral speech
fail to correctly identify speakers under emotional stress. Although
considerable advancements in speaker identification have been made using
convolutional neural networks (CNN), CNNs cannot exploit the spatial
association between low-level features. Inspired by the recent introduction of
capsule networks (CapsNets), which are based on deep learning to overcome the
inadequacy of CNNs in preserving the pose relationship between low-level
features with their pooling technique, this study investigates the performance
of using CapsNets in identifying speakers from emotional speech recordings. A
CapsNet-based speaker identification model is proposed and evaluated using
three distinct speech databases, i.e., the Emirati Speech Database, SUSAS
Dataset, and RAVDESS (open-access). The proposed model is also compared to
baseline systems. Experimental results demonstrate that the novel proposed
CapsNet model trains faster and provides better results over current
state-of-the-art schemes. The effect of the routing algorithm on speaker
identification performance was also studied by varying the number of
iterations, both with and without a decoder network.
