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While many researchers in the speaker recognition area have started toreplace the former classical state-of-the-art methods with deep learningtechniques, some of the traditional i-vector-based methods are stillstate-of-the-art in the context of text-independent speaker verification.Google's Generalized End-to-End Loss for Speaker Verification (GE2E), a deeplearning-based technique using long short-term memory units, has recentlygained a lot of attention due to its speed in convergence and generalization.In this study, we aim at further studying the GE2E method and comparingdifferent scenarios in order to investigate all of its aspects. Variousexperiments including the effects of a random sampling of test and enrollmentutterances, test utterance duration, and the number of enrollment utterancesare discussed in this article. Furthermore, we compare the GE2E method with thebaseline state-of-the-art i-vector-based methods for text-independent speakerverification and show that it outperforms them by resulting in lower errorrates while being end-to-end and requiring less training time for convergence.

An Empirical Study on Text-Independent Speaker Verification based on the GE2E Method