  This paper presents a neural method for distant speech recognition (DSR) that
jointly separates and diarizes speech mixtures without supervision by isolated
signals. A standard separation method for multi-talker DSR is a statistical
multichannel method called guided source separation (GSS). While GSS does not
require signal-level supervision, it relies on speaker diarization results to
handle unknown numbers of active speakers. To overcome this limitation, we
introduce and train a neural inference model in a weakly-supervised manner,
employing the objective function of a statistical separation method. This
training requires only multichannel mixtures and their temporal annotations of
speaker activities. In contrast to GSS, the trained model can jointly separate
and diarize speech mixtures without any auxiliary information. The experiments
with the AMI corpus show that our method outperforms GSS with oracle
diarization results regarding word error rates. The code is available online.
