Generating expressive and contextually appropriate prosody remains a
challenge for modern text-to-speech (TTS) systems. This is particularly evident
for long, multi-sentence inputs. In this paper, we examine simple extensions to
a Transformer-based FastSpeech-like system, with the goal of improving prosody
for multi-sentence TTS. We find that long context, powerful text features, and
training on multi-speaker data all improve prosody. More interestingly, they
result in synergies. Long context disambiguates prosody, improves coherence,
and plays to the strengths of Transformers. Fine-tuning word-level features
from a powerful language model, such as BERT, appears to profit from more
training data, readily available in a multi-speaker setting. We look into
objective metrics on pausing and pacing and perform thorough subjective
evaluations for speech naturalness. Our main system, which incorporates all the
extensions, achieves consistently strong results, including statistically
significant improvements in speech naturalness over all its competitors.

本文针对长文本合成语音的特殊困难，通过将上下文信息、强大文本特征和多人数据用于 Transformer-based FastSpeech 的简单扩展，改进了合成语音的韵律，其中 BERT 也受益于更多的训练数据。通过客观的停顿和速度指标和全面的主观评估，得出了一种较强的合成语音系统，其中包括所有改进扩展，相对于同类竞品在语音自然度方面表现出显著的提升。

简单有效的多句 TTS 系统，具有表达和连贯的韵律

Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody

While significant progress has been made in the image captioning task, video
description is still in its infancy due to the complex nature of video data.
Generating multi-sentence descriptions for long videos is even more
challenging. Among the main issues are the fluency and coherence of the
generated descriptions, and their relevance to the video. Recently,
reinforcement and adversarial learning based methods have been explored to
improve the image captioning models; however, both types of methods suffer from
a number of issues, e.g. poor readability and high redundancy for RL and
stability issues for GANs. In this work, we instead propose to apply
adversarial techniques during inference, designing a discriminator which
encourages better multi-sentence video description. In addition, we find that a
multi-discriminator "hybrid" design, where each discriminator targets one
aspect of a description, leads to the best results. Specifically, we decouple
the discriminator to evaluate on three criteria: 1) visual relevance to the
video, 2) language diversity and fluency, and 3) coherence across sentences.
Our approach results in more accurate, diverse, and coherent multi-sentence
video descriptions, as shown by automatic as well as human evaluation on the
popular ActivityNet Captions dataset.

本研究提出使用对抗技术在推断过程中设计鉴别器来促进更好的多句子视频描述，并提出一个多鉴别器 “混合” 设计，其中每个鉴别器针对说明书的一个方面，以评估三个标准：与视频的视觉相关性、语言多样性和流畅度以及句子间的连贯性。该方法通过对流行的 ActivityNet 字幕数据集进行的自动化以及人工评估表明，能够产生更准确、多样和连贯的多句子视频说明。