  In this work, we conducted an empirical comparative study of the performance
of text-independent speaker verification in emotional and stressful
environments. This work combined deep models with shallow architecture, which
resulted in novel hybrid classifiers. Four distinct hybrid models were
utilized: deep neural network-hidden Markov model (DNN-HMM), deep neural
network-Gaussian mixture model (DNN-GMM), Gaussian mixture model-deep neural
network (GMM-DNN), and hidden Markov model-deep neural network (HMM-DNN). All
models were based on novel implemented architecture. The comparative study used
three distinct speech datasets: a private Arabic dataset and two public English
databases, namely, Speech Under Simulated and Actual Stress (SUSAS) and Ryerson
Audio-Visual Database of Emotional Speech and Song (RAVDESS). The test results
of the aforementioned hybrid models demonstrated that the proposed HMM-DNN
leveraged the verification performance in emotional and stressful environments.
Results also showed that HMM-DNN outperformed all other hybrid models in terms
of equal error rate (EER) and area under the curve (AUC) evaluation metrics.
The average resulting verification system based on the three datasets yielded
EERs of 7.19%, 16.85%, 11.51%, and 11.90% based on HMM-DNN, DNN-HMM, DNN-GMM,
and GMM-DNN, respectively. Furthermore, we found that the DNN-GMM model
demonstrated the least computational complexity compared to all other hybrid
models in both talking environments. Conversely, the HMM-DNN model required the
greatest amount of training time. Findings also demonstrated that EER and AUC
values depended on the database when comparing average emotional and stressful
performances.
