  Speaker diarization systems are challenged by a trade-off between the
temporal resolution and the fidelity of the speaker representation. By
obtaining a superior temporal resolution with an enhanced accuracy, a
multi-scale approach is a way to cope with such a trade-off. In this paper, we
propose a more advanced multi-scale diarization system based on a multi-scale
diarization decoder. There are two main contributions in this study that
significantly improve the diarization performance. First, we use multi-scale
clustering as an initialization to estimate the number of speakers and obtain
the average speaker representation vector for each speaker and each scale.
Next, we propose the use of 1-D convolutional neural networks that dynamically
determine the importance of each scale at each time step. To handle a variable
number of speakers and overlapping speech, the proposed system can estimate the
number of existing speakers. Our proposed system achieves a state-of-art
performance on the CALLHOME and AMI MixHeadset datasets, with 3.92% and 1.05%
diarization error rates, respectively.
