1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchJinyu Lihttps://www.microsoft.com/en-us/research/people/jinyli/Adversarial Speaker Verification - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="o2n1is86V8"><a href="https://www.microsoft.com/en-us/research/publication/adversarial-speaker-verification/">Adversarial Speaker Verification</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/adversarial-speaker-verification/embed/#?secret=o2n1is86V8" width="600" height="338" title="“Adversarial Speaker Verification” — Microsoft Research" data-secret="o2n1is86V8" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In […]