  In multi-speaker applications is common to have pre-computed models from
enrolled speakers. Using these models to identify the instances in which these
speakers intervene in a recording is the task of speaker tracking. In this
paper, we propose a novel embedding-based speaker tracking method.
Specifically, our design is based on a convolutional neural network that mimics
a typical speaker verification PLDA (probabilistic linear discriminant
analysis) classifier and finds the regions uttered by the target speakers in an
online fashion. The system was studied from two different perspectives:
diarization and tracking; results on both show a significant improvement over
the PLDA baseline under the same experimental conditions. Two standard public
datasets, CALLHOME and DIHARD II single channel, were modified to create
two-speaker subsets with overlapping and non-overlapping regions. We evaluate
the robustness of our supervised approach with models generated from different
segment lengths. A relative improvement of 17% in DER for DIHARD II single
channel shows promising performance. Furthermore, to make the baseline system
similar to speaker tracking, non-target speakers were added to the recordings.
Even in these adverse conditions, our approach is robust enough to outperform
the PLDA baseline.
