  The recently-proposed mixture invariant training (MixIT) is an unsupervised
method for training single-channel sound separation models in the sense that it
does not require ground-truth isolated reference sources. In this paper, we
investigate using MixIT to adapt a separation model on real far-field
overlapping reverberant and noisy speech data from the AMI Corpus. The models
are tested on real AMI recordings containing overlapping speech, and are
evaluated subjectively by human listeners. To objectively evaluate our models,
we also devise a synthetic AMI test set. For human evaluations on real
recordings, we also propose a modification of the standard MUSHRA protocol to
handle imperfect reference signals, which we call MUSHIRA. Holding network
architectures constant, we find that a fine-tuned semi-supervised model yields
the largest SI-SNR improvement, PESQ scores, and human listening ratings across
synthetic and real datasets, outperforming unadapted generalist models trained
on orders of magnitude more data. Our results show that unsupervised learning
through MixIT enables model adaptation on real-world unlabeled spontaneous
speech recordings.
