Generative adversarial network (GAN)-based vocoders have been intensively
studied because they can synthesize high-fidelity audio waveforms faster than
real-time. However, it has been reported that most GANs fail to obtain the
optimal projection for discriminating between real and fake data in the feature
space. In the literature, it has been demonstrated that slicing adversarial
network (SAN), an improved GAN training framework that can find the optimal
projection, is effective in the image generation task. In this paper, we
investigate the effectiveness of SAN in the vocoding task. For this purpose, we
propose a scheme to modify least-squares GAN, which most GAN-based vocoders
adopt, so that their loss functions satisfy the requirements of SAN. Through
our experiments, we demonstrate that SAN can improve the performance of
GAN-based vocoders, including BigVGAN, with small modifications. Our code is
available at this https URL

研究一种修改最小二乘 GAN 的方案以满足切片对抗网络（SAN）要求，通过实验验证切片对抗网络对基于 GAN 的合成器，包括 BigVGAN 的性能改进。

BigVSAN：利用切片对抗网络增强基于 GAN 的神经声码器

BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial  Network

An unsupervised text-to-speech synthesis (TTS) system learns to generate
speech waveforms corresponding to any written sentence in a language by
observing: 1) a collection of untranscribed speech waveforms in that language;
2) a collection of texts written in that language without access to any
transcribed speech. Developing such a system can significantly improve the
availability of speech technology to languages without a large amount of
parallel speech and text data. This paper proposes an unsupervised TTS system
based on an alignment module that outputs pseudo-text and another synthesis
module that uses pseudo-text for training and real text for inference. Our
unsupervised system can achieve comparable performance to the supervised system
in seven languages with about 10-20 hours of speech each. A careful study on
the effect of text units and vocoders has also been conducted to better
understand what factors may affect unsupervised TTS performance. The samples
generated by our models can be found at
this https URL, and our code can be found
at this https URL.

该论文提出了一种基于对齐模块输出伪文本和另一个合成模块使用伪文本进行训练和实际文本进行推断的无监督文本转语音综合系统，可以在每种语言的 10-20 小时语音中实现与监督系统相当的性能，并开展了对文本单元和声码器影响的研究。

无监督文本转语音合成与无监督自动语音识别

Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition

Text to speech (TTS), or speech synthesis, which aims to synthesize
intelligible and natural speech given text, is a hot research topic in speech,
language, and machine learning communities and has broad applications in the
industry. As the development of deep learning and artificial intelligence,
neural network-based TTS has significantly improved the quality of synthesized
speech in recent years. In this paper, we conduct a comprehensive survey on
neural TTS, aiming to provide a good understanding of current research and
future trends. We focus on the key components in neural TTS, including text
analysis, acoustic models and vocoders, and several advanced topics, including
fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS, etc.
We further summarize resources related to TTS (e.g., datasets, opensource
implementations) and discuss future research directions. This survey can serve
both academic researchers and industry practitioners working on TTS.

本文全面调研了神经网络 TTS 在包括文本分析、声学模型、声码器等方面的研究进展，进一步总结了相关资源（数据集、开源实现），并提出了未来的研究方向。