  In this paper, we present a novel speaker diarization system for streaming
on-device applications. In this system, we use a transformer transducer to
detect the speaker turns, represent each speaker turn by a speaker embedding,
then cluster these embeddings with constraints from the detected speaker turns.
Compared with conventional clustering-based diarization systems, our system
largely reduces the computational cost of clustering due to the sparsity of
speaker turns. Unlike other supervised speaker diarization systems which
require annotations of time-stamped speaker labels for training, our system
only requires including speaker turn tokens during the transcribing process,
which largely reduces the human efforts involved in data collection.
