Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). How to control the
intensity of accent in the process of TTS is a very interesting research
direction, and has attracted more and more attention. Recent work design a
speaker-adversarial loss to disentangle the speaker and accent information, and
then adjust the loss weight to control the accent intensity. However, such a
control method lacks interpretability, and there is no direct correlation
between the controlling factor and natural accent intensity. To this end, this
paper propose a new intuitive and explicit accent intensity control scheme for
accented TTS. Specifically, we first extract the posterior probability, called
as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to
quantify the phoneme accent intensity for accented speech, then design a
FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity
expression into account during speech generation. Experiments show that the our
method outperforms the baseline model in terms of accent rendering and
intensity control.

本文提出了一种直观明确的口音强度控制方案，首先从 L1 语音识别模型中提取后验概率，称为 “发音好坏度”，量化有重音的语音的音素重音程度，然后设计了一种基于 FastSpeech2 的 TTS 模型 Ai-TTS，在语音生成过程中考虑口音强度表达。实验证明，我们的方法在口音渲染和强度控制方面优于基线模型。

重音文本转语音的明确强度掌控

Explicit Intensity Control for Accented Text-to-speech

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。