Papers › Generalized End-to-End Loss for Speaker Verification
Generalized End-to-End Loss for Speaker Verification
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno
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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Code
Syntology Ran 4 of 27 code samples harvested from 9 repositories linked to this paper; 23 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it.
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30 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
27 samples harvested; 4 ran; 1 honoured the contract we drafted; 23 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speaker Verification | CALLHOME | GE2E | Cosine EER | 3.55 | #1 of 2 | Archive leaderboard | report |
| Speaker Verification | CALLHOME | Cosine EER | 2.38 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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