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Meta-learning with Latent Space Clustering in Generative Adversarial Network for Speaker Diarization
The performance of most speaker diarization systems with x-vector embeddings is both vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker diarization using generative adversarial network (GAN) with an encoder network (ClusterGAN) to project input x-vectors into a lat...
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| 出版年: | IEEE/ACM Trans Audio Speech Lang Process |
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| 主要な著者: | , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8118028/ https://ncbi.nlm.nih.gov/pubmed/33997106 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/taslp.2021.3061885 |
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