The diffusion model is capable of generating high-quality data through a
probabilistic approach. However, it suffers from the drawback of slow
generation speed due to the requirement of a large number of time steps. To
address this limitation, recent models such as denoising diffusion implicit
models (DDIM) focus on generating samples without directly modeling the
probability distribution, while models like denoising diffusion generative
adversarial networks (GAN) combine diffusion processes with GANs. In the field
of speech synthesis, a recent diffusion speech synthesis model called
DiffGAN-TTS, utilizing the structure of GANs, has been introduced and
demonstrates superior performance in both speech quality and generation speed.
In this paper, to further enhance the performance of DiffGAN-TTS, we propose a
speech synthesis model with two discriminators: a diffusion discriminator for
learning the distribution of the reverse process and a spectrogram
discriminator for learning the distribution of the generated data. Objective
metrics such as structural similarity index measure (SSIM), mel-cepstral
distortion (MCD), F0 root mean squared error (F0 RMSE), short-time objective
intelligibility (STOI), perceptual evaluation of speech quality (PESQ), as well
as subjective metrics like mean opinion score (MOS), are used to evaluate the
performance of the proposed model. The evaluation results show that the
proposed model outperforms recent state-of-the-art models such as FastSpeech2
and DiffGAN-TTS in various metrics. Our implementation and audio samples are
located on GitHub.

通过在扩散模型中引入两个辨别器（扩散辨别器和频谱图辨别器），我们提出了一种音频合成模型，其在各项评估指标中均优于 FastSpeech2 和 DiffGAN-TTS，并通过结构相似性指数、梅尔倒谱失真、F0 均方根误差、短时客观可懂性、语音质量感知评估和主观平均意见得分等客观和主观度量对该模型进行了评估。

使用双判别器对去噪扩散模型进行对抗训练的高保真多说话人 TTS

Adversarial Training of Denoising Diffusion Model Using Dual  Discriminators for High-Fidelity Multi-Speaker TTS

Denoising diffusion probabilistic models (DDPMs) are expressive generative
models that have been used to solve a variety of speech synthesis problems.
However, because of their high sampling costs, DDPMs are difficult to use in
real-time speech processing applications. In this paper, we introduce
DiffGAN-TTS, a novel DDPM-based text-to-speech (TTS) model achieving
high-fidelity and efficient speech synthesis. DiffGAN-TTS is based on denoising
diffusion generative adversarial networks (GANs), which adopt an
adversarially-trained expressive model to approximate the denoising
distribution. We show with multi-speaker TTS experiments that DiffGAN-TTS can
generate high-fidelity speech samples within only 4 denoising steps. We present
an active shallow diffusion mechanism to further speed up inference. A
two-stage training scheme is proposed, with a basic TTS acoustic model trained
at stage one providing valuable prior information for a DDPM trained at stage
two. Our experiments show that DiffGAN-TTS can achieve high synthesis
performance with only 1 denoising step.

本文介绍了一种新的基于 Denoising Diffusion Generative Adversarial Networks 的文本到语音模型 (DiffGAN-TTS)，该模型通过多说话人 TTS 实验表明，仅需 4 个去噪步骤即可生成高保真度语音样本，并提出了一个两阶段训练方案，可在仅 1 个去噪步骤下实现高质量的语音合成性能。