  This paper proposes a novel Attention-based Encoder-Decoder network for
End-to-End Neural speaker Diarization (AED-EEND). In AED-EEND system, we
incorporate the target speaker enrollment information used in target speaker
voice activity detection (TS-VAD) to calculate the attractor, which can
mitigate the speaker permutation problem and facilitate easier model
convergence. In the training process, we propose a teacher-forcing strategy to
obtain the enrollment information using the ground-truth label. Furthermore, we
propose three heuristic decoding methods to identify the enrollment area for
each speaker during the evaluation process. Additionally, we enhance the
attractor calculation network LSTM used in the end-to-end encoder-decoder based
attractor calculation (EEND-EDA) system by incorporating an attention-based
model. By utilizing such an attention-based attractor decoder, our proposed
AED-EEND system outperforms both the EEND-EDA and TS-VAD systems with only 0.5s
of enrollment data.
