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Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model
Distant speaker diarization remains challenging due to difficult acoustic environments, varying numbers of speakers and overlapping speech. Model-driven methods are proposed to exploit the speech source features in multi-channel recordings that help diarization. This paper generalizes a neural model
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