  Collecting audio-text pairs is expensive; however, it is much easier to
access text-only data. Unless using shallow fusion, end-to-end automatic speech
recognition (ASR) models require architecture modifications or additional
training schemes to use text-only data. Inspired by recent advances in
decoder-only language models (LMs), such as GPT-3 and PaLM adopted for
speech-processing tasks, we propose using a decoder-only architecture for ASR
with simple text augmentation. To provide audio information, encoder features
compressed by CTC prediction are used as prompts for the decoder, which can be
regarded as refining CTC prediction using the decoder-only model. Because the
decoder architecture is the same as an autoregressive LM, it is simple to
enhance the model by leveraging external text data with LM training. An
experimental comparison using LibriSpeech and Switchboard shows that our
proposed models with text augmentation training reduced word error rates from
ordinary CTC by 0.3% and 1.4% on LibriSpeech test-clean and testother set,
respectively, and 2.9% and 5.0% on Switchboard and CallHome. The proposed model
had advantage on computational efficiency compared with conventional
encoder-decoder ASR models with a similar parameter setup, and outperformed
them on the LibriSpeech 100h and Switchboard training scenarios.
