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.

翻译：本文提出一种端到端自动语音识别（ASR）系统，该系统可同时利用转写语音数据、纯文本数据或两者的混合数据进行训练。该模型采用集成辅助模块实现基于文本的训练：该模块结合了非自回归多说话人文本到梅尔频谱图生成器与基于生成对抗网络的增强器，以提升频谱图质量。所提系统可在训练过程中动态生成梅尔频谱图，从而利用目标领域的纯文本数据实现ASR模型的领域自适应。实验表明，与仅使用转写语音训练的系统相比，本文提出的训练方法显著提升了ASR准确率；在适应质量与训练速度方面，该方法亦优于级联式带声码器的文本转语音系统。