  While recent research advances in speaker diarization mostly focus on
improving the quality of diarization results, there is also an increasing
interest in improving the efficiency of diarization systems. In this paper, we
demonstrate that a multi-stage clustering strategy that uses different
clustering algorithms for input of different lengths can address multi-faceted
challenges of on-device speaker diarization applications. Specifically, a
fallback clusterer is used to handle short-form inputs; a main clusterer is
used to handle medium-length inputs; and a pre-clusterer is used to compress
long-form inputs before they are processed by the main clusterer. Both the main
clusterer and the pre-clusterer can be configured with an upper bound of the
computational complexity to adapt to devices with different resource
constraints. This multi-stage clustering strategy is critical for streaming
on-device speaker diarization systems, where the budgets of CPU, memory and
battery are tight.
