  The recently proposed conformer architecture has been successfully used for
end-to-end automatic speech recognition (ASR) architectures achieving
state-of-the-art performance on different datasets. To our best knowledge, the
impact of using conformer acoustic model for hybrid ASR is not investigated. In
this paper, we present and evaluate a competitive conformer-based hybrid model
training recipe. We study different training aspects and methods to improve
word-error-rate as well as to increase training speed. We apply time
downsampling methods for efficient training and use transposed convolutions to
upsample the output sequence again. We conduct experiments on Switchboard 300h
dataset and our conformer-based hybrid model achieves competitive results
compared to other architectures. It generalizes very well on Hub5'01 test set
and outperforms the BLSTM-based hybrid model significantly.
