  Speaker diarization for real-life scenarios is an extremely challenging
problem. Widely used clustering-based diarization approaches perform rather
poorly in such conditions, mainly due to the limited ability to handle
overlapping speech. We propose a novel Target-Speaker Voice Activity Detection
(TS-VAD) approach, which directly predicts an activity of each speaker on each
time frame. TS-VAD model takes conventional speech features (e.g., MFCC) along
with i-vectors for each speaker as inputs. A set of binary classification
output layers produces activities of each speaker. I-vectors can be estimated
iteratively, starting with a strong clustering-based diarization. We also
extend the TS-VAD approach to the multi-microphone case using a simple
attention mechanism on top of hidden representations extracted from the
single-channel TS-VAD model. Moreover, post-processing strategies for the
predicted speaker activity probabilities are investigated. Experiments on the
CHiME-6 unsegmented data show that TS-VAD achieves state-of-the-art results
outperforming the baseline x-vector-based system by more than 30% Diarization
Error Rate (DER) abs.
