  Recent analysis on speech emotion recognition has made considerable advances
with the use of MFCCs spectrogram features and the implementation of neural
network approaches such as convolutional neural networks (CNNs). Capsule
networks (CapsNet) have gained gratitude as alternatives to CNNs with their
larger capacities for hierarchical representation. To address these issues,
this research introduces a text-independent and speaker-independent SER novel
architecture, where a dual-channel long short-term memory compressed-CapsNet
(DC-LSTM COMP-CapsNet) algorithm is proposed based on the structural features
of CapsNet. Our proposed novel classifier can ensure the energy efficiency of
the model and adequate compression method in speech emotion recognition, which
is not delivered through the original structure of a CapsNet. Moreover, the
grid search approach is used to attain optimal solutions. Results witnessed an
improved performance and reduction in the training and testing running time.
The speech datasets used to evaluate our algorithm are: Arabic Emirati-accented
corpus, English speech under simulated and actual stress corpus, English
Ryerson audio-visual database of emotional speech and song corpus, and
crowd-sourced emotional multimodal actors dataset. This work reveals that the
optimum feature extraction method compared to other known methods is MFCCs
delta-delta. Using the four datasets and the MFCCs delta-delta, DC-LSTM
COMP-CapsNet surpasses all the state-of-the-art systems, classical classifiers,
CNN, and the original CapsNet. Using the Arabic Emirati-accented corpus, our
results demonstrate that the proposed work yields average emotion recognition
accuracy of 89.3% compared to 84.7%, 82.2%, 69.8%, 69.2%, 53.8%, 42.6%, and
31.9% based on CapsNet, CNN, support vector machine, multi-layer perceptron,
k-nearest neighbor, radial basis function, and naive Bayes, respectively.
