  In this paper, we propose methods for improving the modeling performance of a
Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although
the text encoder and audio decoder handle different types and lengths of data
(i.e., text and audio), the TNA-TTS models are not designed considering these
variations. Therefore, to improve the modeling performance of the TNA-TTS model
we propose a hierarchical Transformer structure-based text encoder and audio
decoder that are designed to accommodate the characteristics of each module.
For the text encoder, we constrain each self-attention layer so the encoder
focuses on a text sequence from the local to the global scope. Conversely, the
audio decoder constrains its self-attention layers to focus in the reverse
direction, i.e., from global to local scope. Additionally, we further improve
the pitch modeling accuracy of the audio decoder by providing sentence and
word-level pitch as conditions. Various objective and subjective evaluations
verified that the proposed method outperformed the baseline TNA-TTS.
