  We propose an end-to-end Automatic Speech Recognition (ASR) system that can
be trained on transcribed speech data, text-only data, or a mixture of both.
The proposed model uses an integrated auxiliary block for text-based training.
This block combines a non-autoregressive multi-speaker text-to-mel-spectrogram
generator with a GAN-based enhancer to improve the spectrogram quality. The
proposed system can generate a mel-spectrogram dynamically during training. It
can be used to adapt the ASR model to a new domain by using text-only data from
this domain. We demonstrate that the proposed training method significantly
improves ASR accuracy compared to the system trained on transcribed speech
only. It also surpasses cascade TTS systems with the vocoder in the adaptation
quality and training speed.
