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Recently, speech representation learning has improved many speech-relatedtasks such as speech recognition, speech classification, and speech-to-texttranslation. However, all the above tasks are in the direction of speechunderstanding, but for the inverse direction, speech synthesis, the potentialof representation learning is yet to be realized, due to the challenging natureof generating high-quality speech. To address this problem, we propose ourframework, Alignment-Aware Acoustic-Text Pretraining (A$^3$T), whichreconstructs masked acoustic signals with text input and acoustic-textalignment during training. In this way, the pretrained model can generate highquality reconstructed spectrogram, which can be applied to the speech editingand unseen speaker TTS directly. Experiments show A$^3$T outperforms SOTAmodels on speech editing, and improves multi-speaker speech synthesis withoutthe external speaker verification model.

A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing