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We propose a novel neural speaker diarization system using memory-awaremulti-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 bothefficiency and performance. Next, we further decrease the memory occupation ofdecoding by incorporating input features fusion and then employ a multi-headattention mechanism to capture features at different levels. NSD-MS2S achieveda macro diarization error rate (DER) of 15.9% on the CHiME-7 EVAL set, whichsignifies a relative improvement of 49% over the official baseline system, andis the key technique for us to achieve the best performance for the main trackof CHiME-7 DASR Challenge. Additionally, we introduce a deep interactive module(DIM) in MA-MSE module to better retrieve a cleaner and more discriminativemulti-speaker embedding, enabling the current model to outperform the system weused in the CHiME-7 DASR Challenge. Our code will be available athttps://github.com/liyunlongaaa/NSD-MS2S.

Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture