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We propose an end-to-end Automatic Speech Recognition (ASR) system that canbe 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-spectrogramgenerator with a GAN-based enhancer to improve the spectrogram quality. Theproposed system can generate a mel-spectrogram dynamically during training. Itcan be used to adapt the ASR model to a new domain by using text-only data fromthis domain. We demonstrate that the proposed training method significantlyimproves ASR accuracy compared to the system trained on transcribed speechonly. It also surpasses cascade TTS systems with the vocoder in the adaptationquality and training speed.

Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator