  Benefiting from the development of deep learning, text-to-speech (TTS)
techniques using clean speech have achieved significant performance
improvements. The data collected from real scenes often contains noise and
generally needs to be denoised by speech enhancement models. Noise-robust TTS
models are often trained using the enhanced speech, which thus suffer from
speech distortion and background noise that affect the quality of the
synthesized speech. Meanwhile, it was shown that self-supervised pre-trained
models exhibit excellent noise robustness on many speech tasks, implying that
the learned representation has a better tolerance for noise perturbations. In
this work, we therefore explore pre-trained models to improve the noise
robustness of TTS models. Based on HiFi-GAN, we first propose a
representation-to-waveform vocoder, which aims to learn to map the
representation of pre-trained models to the waveform. We then propose a
text-to-representation FastSpeech2 model, which aims to learn to map text to
pre-trained model representations. Experimental results on the LJSpeech and
LibriTTS datasets show that our method outperforms those using speech
enhancement methods in both subjective and objective metrics. Audio samples are
available at: https://zqs01.github.io/rep2wav.
