  In this paper, we present the submitted system for the second DIHARD Speech
Diarization Challenge from the DKULENOVO team. Our diarization system includes
multiple modules, namely voice activity detection (VAD), segmentation, speaker
embedding extraction, similarity scoring, clustering, resegmentation and
overlap detection. For each module, we explore different techniques to enhance
performance. Our final submission employs the ResNet-LSTM based VAD, the Deep
ResNet based speaker embedding, the LSTM based similarity scoring and spectral
clustering. Variational Bayes (VB) diarization is applied in the resegmentation
stage and overlap detection also brings slight improvement. Our proposed system
achieves 18.84% DER in Track1 and 27.90% DER in Track2. Although our systems
have reduced the DERs by 27.5% and 31.7% relatively against the official
baselines, we believe that the diarization task is still very difficult.
