Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). Accented TTS synthesis
is challenging as L2 is different from L1 in both in terms of phonetic
rendering and prosody pattern. Furthermore, there is no easy solution to the
control of the accent intensity in an utterance. In this work, we propose a
neural TTS architecture, that allows us to control the accent and its intensity
during inference. This is achieved through three novel mechanisms, 1) an accent
variance adaptor to model the complex accent variance with three prosody
controlling factors, namely pitch, energy and duration; 2) an accent intensity
modeling strategy to quantify the accent intensity; 3) a consistency constraint
module to encourage the TTS system to render the expected accent intensity at a
fine level. Experiments show that the proposed system attains superior
performance to the baseline models in terms of accent rendering and intensity
control. To our best knowledge, this is the first study of accented TTS
synthesis with explicit intensity control.

该论文提出了一种神经网络 TTS 架构，可在推理期间控制重音和重音强度，并以三种新机制实现，即使用方案控制因子建模复杂的重音变异，量化重音强度的强调强度建模策略以及一致性约束模块。实验为该模型的重音渲染和强度控制性能提供了证明，并证明其是有史以来第一篇关于具有明确强度控制的重音 TTS 合成的研究。