  In this paper, we carry out an analysis on the use of speech separation
guided diarization (SSGD) in telephone conversations. SSGD performs diarization
by separating the speakers signals and then applying voice activity detection
on each estimated speaker signal. In particular, we compare two low-latency
speech separation models. Moreover, we show a post-processing algorithm that
significantly reduces the false alarm errors of a SSGD pipeline. We perform our
experiments on two datasets: Fisher Corpus Part 1 and CALLHOME, evaluating both
separation and diarization metrics. Notably, our SSGD DPRNN-based online model
achieves 11.1% DER on CALLHOME, comparable with most state-of-the-art
end-to-end neural diarization models despite being trained on an order of
magnitude less data and having considerably lower latency, i.e., 0.1 vs. 10
seconds. We also show that the separated signals can be readily fed to a speech
recognition back-end with performance close to the oracle source signals.
