  While end-to-end ASR systems have proven competitive with the conventional
hybrid approach, they are prone to accuracy degradation when it comes to noisy
and low-resource conditions. In this paper, we argue that, even in such
difficult cases, some end-to-end approaches show performance close to the
hybrid baseline. To demonstrate this, we use the CHiME-6 Challenge data as an
example of challenging environments and noisy conditions of everyday speech. We
experimentally compare and analyze CTC-Attention versus RNN-Transducer
approaches along with RNN versus Transformer architectures. We also provide a
comparison of acoustic features and speech enhancements. Besides, we evaluate
the effectiveness of neural network language models for hypothesis re-scoring
in low-resource conditions. Our best end-to-end model based on RNN-Transducer,
together with improved beam search, reaches quality by only 3.8% WER abs. worse
than the LF-MMI TDNN-F CHiME-6 Challenge baseline. With the Guided Source
Separation based training data augmentation, this approach outperforms the
hybrid baseline system by 2.7% WER abs. and the end-to-end system best known
before by 25.7% WER abs.
