  In this paper, we present Hitachi and Paderborn University's joint effort for
automatic speech recognition (ASR) in a dinner party scenario. The main
challenges of ASR systems for dinner party recordings obtained by multiple
microphone arrays are (1) heavy speech overlaps, (2) severe noise and
reverberation, (3) very natural conversational content, and possibly (4)
insufficient training data. As an example of a dinner party scenario, we have
chosen the data presented during the CHiME-5 speech recognition challenge,
where the baseline ASR had a 73.3% word error rate (WER), and even the best
performing system at the CHiME-5 challenge had a 46.1% WER. We extensively
investigated a combination of the guided source separation-based speech
enhancement technique and an already proposed strong ASR backend and found that
a tight combination of these techniques provided substantial accuracy
improvements. Our final system achieved WERs of 39.94% and 41.64% for the
development and evaluation data, respectively, both of which are the best
published results for the dataset. We also investigated with additional
training data on the official small data in the CHiME-5 corpus to assess the
intrinsic difficulty of this ASR task.
