  Source separation can improve automatic speech recognition (ASR) under
multi-party meeting scenarios by extracting single-speaker signals from
overlapped speech. Despite the success of self-supervised learning models in
single-channel source separation, most studies have focused on simulated
setups. In this paper, seven SSL models were compared on both simulated and
real-world corpora. Then, we propose to integrate the best-performing model
WavLM into an automatic transcription system through a novel iterative source
selection method. To improve real-world performance, time-domain unsupervised
mixture invariant training was adapted to the time-frequency domain.
Experiments showed that in the transcription system when source separation was
inserted before an ASR model fine-tuned on separated speech, absolute
reductions of 1.9% and 1.5% in concatenated minimum-permutation word error rate
for an unknown number of speakers (cpWER-us) were observed on the AMI dev and
test sets.
