  Speaker diarization is the process of labeling different speakers in a speech
signal. Deep speaker embeddings are generally extracted from short speech
segments and clustered to determine the segments belong to same speaker
identity. The x-vector, which embeds segment-level speaker characteristics by
statistically pooling frame-level representations, is one of the most widely
used deep speaker embeddings in speaker diarization. Multi-scale aggregation,
which employs multi-scale representations from different layers, has recently
successfully been used in short duration speaker verification. In this paper,
we investigate a multi-scale aggregation approach in an x-vector embedding
framework for speaker diarization by exploiting multiple statistics pooling
layers from different frame-level layers. Thus, it is expected that x-vectors
with multi-scale aggregation have the potential to capture meaningful speaker
characteristics from short segments, effectively taking advantage of different
information at multiple layers. Experimental evaluation on the CALLHOME dataset
showed that our approach provides substantial improvement over the baseline
x-vectors.
