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In this work, we propose deep latent space clustering for speaker diarization using generative adversarial network (GAN) backprojection with the help of an encoder network.
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“Developing on-line speaker diarization system,”
Dimitrios Dimitriadis and Petr Fousek, · 2017
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“Towards k-means-friendly spaces: Simultaneous deep learning and clustering,”
Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong, · 2017
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Ishaan Gulrajani et al., · 2017
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Zachary C Lipton and Subarna Tripathi, · 2017
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“Diarization is hard: Some experiences and lessons learned for the JHU team in the inaugural DIHARD challenge,”
Gregory Sell et al., · 2018
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“Speaker diarization with LSTM,”
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, and Ignacio Lopz Moreno, · 2018
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“Fully supervised speaker diarization,”
Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, and Chong Wang, · 2019
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“Speaker diarisation using 2D self-attentive combination of embeddings,”
Guangzhi Sun, Chao Zhang, and Philip C Woodland, · 2019
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“Enhancements for audio-only diarization systems,”
Dimitrios Dimitriadis, · 2019
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“ClusterGAN: Latent space clustering in generative adversarial networks,”
Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan, · 2019
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“The Second DIHARD challenge: System Description for USC-SAIL Team,”
Tae Jin Park et al., · 2019
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