  Significant performance degradation of automatic speech recognition (ASR)
systems is observed when the audio signal contains cross-talk. One of the
recently proposed approaches to solve the problem of multi-speaker ASR is the
deep clustering (DPCL) approach. Combining DPCL with a state-of-the-art hybrid
acoustic model, we obtain a word error rate (WER) of 16.5 % on the commonly
used wsj0-2mix dataset, which is the best performance reported thus far to the
best of our knowledge. The wsj0-2mix dataset contains simulated cross-talk
where the speech of multiple speakers overlaps for almost the entire utterance.
In a more realistic ASR scenario the audio signal contains significant portions
of single-speaker speech and only part of the signal contains speech of
multiple competing speakers. This paper investigates obstacles of applying DPCL
as a preprocessing method for ASR in such a scenario of sparsely overlapping
speech. To this end we present a data simulation approach, closely related to
the wsj0-2mix dataset, generating sparsely overlapping speech datasets of
arbitrary overlap ratio. The analysis of applying DPCL to sparsely overlapping
speech is an important interim step between the fully overlapping datasets like
wsj0-2mix and more realistic ASR datasets, such as CHiME-5 or AMI.
