  Previous works on neural text-to-speech (TTS) have been addressed on limited
speed in training and inference time, robustness for difficult synthesis
conditions, expressiveness, and controllability. Although several approaches
resolve some limitations, there has been no attempt to solve all weaknesses at
once. In this paper, we propose STYLER, an expressive and controllable TTS
framework with high-speed and robust synthesis. Our novel audio-text aligning
method called Mel Calibrator and excluding autoregressive decoding enable rapid
training and inference and robust synthesis on unseen data. Also, disentangled
style factor modeling under supervision enlarges the controllability in
synthesizing process leading to expressive TTS. On top of it, a novel noise
modeling pipeline using domain adversarial training and Residual Decoding
empowers noise-robust style transfer, decomposing the noise without any
additional label. Various experiments demonstrate that STYLER is more effective
in speed and robustness than expressive TTS with autoregressive decoding and
more expressive and controllable than reading style non-autoregressive TTS.
Synthesis samples and experiment results are provided via our demo page, and
code is available publicly.
