  This paper presents an exploration of end-to-end automatic speech recognition
systems (ASR) for the largest open-source Russian language data set -- OpenSTT.
We evaluate different existing end-to-end approaches such as joint
CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with
the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model. For the
three available validation sets (phone calls, YouTube, and books), our best
end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%,
respectively. Under the same conditions, the hybridASR system demonstrates
33.5%, 20.9%, and 18.6% WER.
