  This paper describes a variational auto-encoder based non-autoregressive
text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based
on the sequence-to-sequence architecture can generate high-quality speech, but
their sequential decoding process can be time-consuming. Recently,
non-autoregressive TTS (NAR-TTS) models have been shown to be more efficient
with the parallel decoding process. However, these NAR-TTS models rely on
phoneme-level durations to generate a hard alignment between the text and the
spectrogram. Obtaining duration labels, either through forced alignment or
knowledge distillation, is cumbersome. Furthermore, hard alignment based on
phoneme expansion can degrade the naturalness of the synthesized speech. In
contrast, the proposed model of VAENAR-TTS is an end-to-end approach that does
not require phoneme-level durations. The VAENAR-TTS model does not contain
recurrent structures and is completely non-autoregressive in both the training
and inference phases. Based on the VAE architecture, the alignment information
is encoded in the latent variable, and attention-based soft alignment between
the text and the latent variable is used in the decoder to reconstruct the
spectrogram. Experiments show that VAENAR-TTS achieves state-of-the-art
synthesis quality, while the synthesis speed is comparable with other NAR-TTS
models.
