  Multi-speaker speech recognition of unsegmented recordings has diverse
applications such as meeting transcription and automatic subtitle generation.
With technical advances in systems dealing with speech separation, speaker
diarization, and automatic speech recognition (ASR) in the last decade, it has
become possible to build pipelines that achieve reasonable error rates on this
task. In this paper, we propose an end-to-end modular system for the LibriCSS
meeting data, which combines independently trained separation, diarization, and
recognition components, in that order. We study the effect of different
state-of-the-art methods at each stage of the pipeline, and report results
using task-specific metrics like SDR and DER, as well as downstream WER.
Experiments indicate that the problem of overlapping speech for diarization and
ASR can be effectively mitigated with the presence of a well-trained separation
module. Our best system achieves a speaker-attributed WER of 12.7%, which is
close to that of a non-overlapping ASR.
