  In this paper, we present the submitted system for the third DIHARD Speech
Diarization Challenge from the DKU-Duke-Lenovo team. Our system consists of
several modules: voice activity detection (VAD), segmentation, speaker
embedding extraction, attentive similarity scoring, agglomerative hierarchical
clustering. In addition, the target speaker VAD (TSVAD) is used for the phone
call data to further improve the performance. Our final submitted system
achieves a DER of 15.43% for the core evaluation set and 13.39% for the full
evaluation set on task 1, and we also get a DER of 21.63% for core evaluation
set and 18.90% for full evaluation set on task 2.
