Generalized End-to-End Loss for Speaker Verification

Paper

[Link] to arXiv paper

Authors

Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno

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.

Updates

There had been a typo in Eq. (6) of the original paper. The correct equation should be:

Slides

[PPTX] for ICASSP 2018 presentation

Implementations

The implementations of the GE2E loss has heavy dependencies on Google's internal libraries, thus CANNOT be open sourced.

We are aware of several third-party implementations of this work, such as:

  • TensorFlow implementation by Janghyun1230
  • PyTorch implementaion by HarryVolek
  • However, please note that we are NOT responsible for the correctness of any third-party implementations. Please use your own judgement to decide whether you want to use these implementations.

    Applications

    The speaker recognition and embedding technique based on this paper has been applied to multiple domains, including:

  • Education
  • Source separation
  • Speaker diarization
  • Supervised speaker diarization
  • Text-to-speech synthesis
  • Speech-to-speech translation
  • Lecture