Generalized End-to-End Loss for Speaker Verification

28 Oct 2017Li WanQuan WangAlan PapirIgnacio 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... (read more)

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