MoBoAligner: a Neural Alignment Model for Non-autoregressive TTS with Monotonic Boundary Search
Journal: arXiv (Cornell University)
Year: 2020

To speed up the inference of neural speech synthesis, non-autoregressive models receive increasing attention recently. In non-autoregressive models, additional durations of text tokens are required to make a hard alignment between the encoder and the decoder. The duration-based alignment plays a crucial role since it controls the correspondence between text tokens and spectrum frames and determines the rhythm and speed of synthesized audio. To get better duration-based alignment and improve the