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This paper proposes VARA-TTS, a non-autoregressive (non-AR) text-to-speech(TTS) model using a very deep Variational Autoencoder (VDVAE) with ResidualAttention mechanism, which refines the textual-to-acoustic alignmentlayer-wisely. Hierarchical latent variables with different temporal resolutionsfrom the VDVAE are used as queries for residual attention module. By leveragingthe coarse global alignment from previous attention layer as an extra input,the following attention layer can produce a refined version of alignment. Thisamortizes the burden of learning the textual-to-acoustic alignment amongmultiple attention layers and outperforms the use of only a single attentionlayer in robustness. An utterance-level speaking speed factor is computed by ajointly-trained speaking speed predictor, which takes the mean-pooled latentvariables of the coarsest layer as input, to determine number of acousticframes at inference. Experimental results show that VARA-TTS achieves slightlyinferior speech quality to an AR counterpart Tacotron 2 but anorder-of-magnitude speed-up at inference; and outperforms an analogous non-ARmodel, BVAE-TTS, in terms of speech quality.

VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention