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

Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis