This paper presents our work on phrase break prediction in the context of
end-to-end TTS systems, motivated by the following questions: (i) Is there any
utility in incorporating an explicit phrasing model in an end-to-end TTS
system?, and (ii) How do you evaluate the effectiveness of a phrasing model in
an end-to-end TTS system? In particular, the utility and effectiveness of
phrase break prediction models are evaluated in in the context of childrens
story synthesis, using listener comprehension. We show by means of perceptual
listening evaluations that there is a clear preference for stories synthesized
after predicting the location of phrase breaks using a trained phrasing model,
over stories directly synthesized without predicting the location of phrase
breaks.

该论文研究了在端到端 TTS 系统的上下文中的短语断句预测问题，并评估了在儿童故事合成中使用预测短语断句的实用性和有效性，结果表明使用训练好的短语模型预测短语断句的故事比直接合成的故事更受听众理解。

端到端 TTS 系统中对说话者无关语调断句模型的研究

An investigation of speaker independent phrase break models in  End-to-End TTS systems

End-to-end text-to-speech (TTS) systems have been developed for European
languages like English and Spanish with state-of-the-art speech quality,
prosody, and naturalness. However, development of end-to-end TTS for Indian
languages is lagging behind in terms of quality. The challenges involved in
such a task are: 1) scarcity of quality training data; 2) low efficiency during
training and inference; 3) slow convergence in the case of large vocabulary
size. In our work reported in this paper, we have investigated the use of
fine-tuning the English-pretrained Tacotron2 model with limited Sanskrit data
to synthesize natural sounding speech in Sanskrit in low resource settings. Our
experiments show encouraging results, achieving an overall MOS of 3.38 from 37
evaluators with good Sanskrit spoken knowledge. This is really a very good
result, considering the fact that the speech data we have used is of duration
2.5 hours only.

本文研究使用 fine-tuning 改进了英语预训练的 Tacotron2 模型，以有限的梵语数据在资源匮乏的环境下合成自然语音，并取得了令人鼓舞的结果。

使用 Tacotron2、WaveGlow 和迁移学习的低资源端到端梵语 TTS

Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning

The recent text-to-speech (TTS) has achieved quality comparable to that of
humans; however, its application in spoken dialogue has not been widely
studied. This study aims to realize a TTS that closely resembles human
dialogue. First, we record and transcribe actual spontaneous dialogues. Then,
the proposed dialogue TTS is trained in two stages: first stage, variational
autoencoder (VAE)-VITS or Gaussian mixture variational autoencoder (GMVAE)-VITS
is trained, which introduces an utterance-level latent variable into
variational inference with adversarial learning for end-to-end text-to-speech
(VITS), a recently proposed end-to-end TTS model. A style encoder that extracts
a latent speaking style representation from speech is trained jointly with TTS.
In the second stage, a style predictor is trained to predict the speaking style
to be synthesized from dialogue history. During inference, by passing the
speaking style representation predicted by the style predictor to
VAE/GMVAE-VITS, speech can be synthesized in a style appropriate to the context
of the dialogue. Subjective evaluation results demonstrate that the proposed
method outperforms the original VITS in terms of dialogue-level naturalness.

本研究旨在实现一种接近于人类对话的 TTS，通过基于 VAE/GMVAE-VITS 的训练，利用上下文信息来综合预测所需样式的语音合成， 实验结果表明，该方法在对话级别的自然度方面优于原本的 VITS。

基于言语风格潜在表示的端到端文本转语音技术：基于自然对话的研究

End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue

This paper presents our MSXF TTS system for Task 3.1 of the Audio Deep
Synthesis Detection (ADD) Challenge 2022. We use an end to end text to speech
system, and add a constraint loss to the system when training stage. The end to
end TTS system is VITS, and the pre-training self-supervised model is wav2vec
2.0. And we also explore the influence of the speech speed and volume in
spoofing. The faster speech means the less the silence part in audio, the
easier to fool the detector. We also find the smaller the volume, the better
spoofing ability, though we normalize volume for submission. Our team is
identified as C2, and we got the fourth place in the challenge.

本文介绍了我们的 MSXF TTS 系统，使用了端到端 TTS 系统，并在训练阶段添加了一个约束损失。我们还研究了语音速度和音量对欺诈的影响。我们的团队在 ADD 挑战赛上获得了第四名。