  In recent years, there have been studies to further improve the end-to-end
neural speaker diarization (EEND) systems. This letter proposes the EEND-DEMUX
model, a novel framework utilizing demultiplexed speaker embeddings. In this
work, we focus on disentangling speaker-relevant information in the latent
space and then transform each separated latent variable into its corresponding
speech activity. EEND-DEMUX can directly obtain separated speaker embeddings
through the demultiplexing operation in the inference phase without an external
speaker diarization system, an embedding extractor, or a heuristic decoding
technique. Furthermore, we employ a multi-head cross-attention mechanism to
capture the correlation between mixture and separated speaker embeddings
effectively. We formulate three loss functions based on matching,
orthogonality, and sparsity constraints to learn robust demultiplexed speaker
embeddings. The experimental results on the LibriMix dataset show consistently
improved performance in both a fixed and flexible number of speakers scenarios.
