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GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION

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Citation Author(s):
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno
Submitted by:
Quan Wang
Last updated:
18 April 2018 - 11:00am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters Name:
Quan Wang
Paper Code:
SP-L5.3

Abstract 

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 end-to-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. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. 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.

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Dataset Files

ICASSP 2018 GE2E.pptx

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