A speaker-embedding-free enhancement model is extended to do both conventional denoising and target-speaker extraction with a zero-enrollment trick, plus a consistency loss that pairs two enrollment utterances of the same speaker to improve robustness.
SEGAN: Speech enhancement generative adversarial network,
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Unified Architecture and Unsupervised Speech Disentanglement for Speaker Embedding-Free Enrollment in Personalized Speech Enhancement
A speaker-embedding-free enhancement model is extended to do both conventional denoising and target-speaker extraction with a zero-enrollment trick, plus a consistency loss that pairs two enrollment utterances of the same speaker to improve robustness.