Generalized End-to-End Loss for Speaker Verification

ABSTRACT In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based endto-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. We also introduce the MultiReader technique, which allows us to do domain adaptation -- training a more accurate model that supports multiple keywords (i.e., "OK Google" and "Hey Google") as well as multiple dialects. Background 1. INTRODUCTION Speaker verification (SV) is the process of verifying whether an utterance belongs to a specific speaker, based on that speaker's known utterances (i.e., enrollment utterances), with applications such as Voice Match [1, 2].