  Recently, self-supervised pretraining has achieved impressive results in
end-to-end (E2E) automatic speech recognition (ASR). However, the dominant
sequence-to-sequence (S2S) E2E model is still hard to fully utilize the
self-supervised pre-training methods because its decoder is conditioned on
acoustic representation thus cannot be pretrained separately. In this paper, we
propose a pretrained Transformer (Preformer) S2S ASR architecture based on
hybrid CTC/attention E2E models to fully utilize the pretrained acoustic models
(AMs) and language models (LMs). In our framework, the encoder is initialized
with a pretrained AM (wav2vec2.0). The Preformer leverages CTC as an auxiliary
task during training and inference. Furthermore, we design a one-cross decoder
(OCD), which relaxes the dependence on acoustic representations so that it can
be initialized with pretrained LM (DistilGPT2). Experiments are conducted on
the AISHELL-1 corpus and achieve a $4.6\%$ character error rate (CER) on the
test set. Compared with our vanilla hybrid CTC/attention Transformer baseline,
our proposed CTC/attention-based Preformer yields $27\%$ relative CER
reduction. To the best of our knowledge, this is the first work to utilize both
pretrained AM and LM in a S2S ASR system.

Audio and Speech Processing;Computation and Language;Sound