  We propose a novel neural speaker diarization system using memory-aware
multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S),
which integrates the strengths of memory-aware multi-speaker embedding (MA-MSE)
and sequence-to-sequence (Seq2Seq) architecture, leading to improvement in both
efficiency and performance. Next, we further decrease the memory occupation of
decoding by incorporating input features fusion and then employ a multi-head
attention mechanism to capture features at different levels. NSD-MS2S achieved
a macro diarization error rate (DER) of 15.9% on the CHiME-7 EVAL set, which
signifies a relative improvement of 49% over the official baseline system, and
is the key technique for us to achieve the best performance for the main track
of CHiME-7 DASR Challenge. Additionally, we introduce a deep interactive module
(DIM) in MA-MSE module to better retrieve a cleaner and more discriminative
multi-speaker embedding, enabling the current model to outperform the system we
used in the CHiME-7 DASR Challenge. Our code will be available at
https://github.com/liyunlongaaa/NSD-MS2S.
