  While many researchers in the speaker recognition area have started to
replace the former classical state-of-the-art methods with deep learning
techniques, some of the traditional i-vector-based methods are still
state-of-the-art in the context of text-independent speaker verification.
Google's Generalized End-to-End Loss for Speaker Verification (GE2E), a deep
learning-based technique using long short-term memory units, has recently
gained a lot of attention due to its speed in convergence and generalization.
In this study, we aim at further studying the GE2E method and comparing
different scenarios in order to investigate all of its aspects. Various
experiments including the effects of a random sampling of test and enrollment
utterances, test utterance duration, and the number of enrollment utterances
are discussed in this article. Furthermore, we compare the GE2E method with the
baseline state-of-the-art i-vector-based methods for text-independent speaker
verification and show that it outperforms them by resulting in lower error
rates while being end-to-end and requiring less training time for convergence.
