  Since diarization and source separation of meeting data are closely related
tasks, we here propose an approach to perform the two objectives jointly. It
builds upon the target-speaker voice activity detection (TS-VAD) diarization
approach, which assumes that initial speaker embeddings are available. We
replace the final combined speaker activity estimation network of TS-VAD with a
network that produces speaker activity estimates at a time-frequency
resolution. Those act as masks for source extraction, either via masking or via
beamforming. The technique can be applied both for single-channel and
multi-channel input and, in both cases, achieves a new state-of-the-art word
error rate (WER) on the LibriCSS meeting data recognition task. We further
compute speaker-aware and speaker-agnostic WERs to isolate the contribution of
diarization errors to the overall WER performance.
