ECTSpeech：通过简易一致性调整增强高效语音合成
ECTSpeech: Enhancing Efficient Speech Synthesis via Easy Consistency Tuning
AI总结：
ECTSpeech提出首个将简易一致性调整融入语音合成的单步框架，通过逐步收紧约束并设计多尺度门控模块，在LJSpeech上以低训练成本实现与最先进方法相当的音频质量。
摘要：
扩散模型在语音合成中展现了卓越的性能，但通常需要多步采样，导致推理效率低下。近期研究通过将扩散模型蒸馏为一致性模型来解决这一问题，从而实现高效的单步生成。然而，这些方法引入了额外的训练成本，并且严重依赖预训练教师模型的性能。在本文中，我们提出了ECTSpeech，一个简单而有效的单步语音合成框架，首次将简易一致性调整（ECT）策略融入语音合成。通过对预训练的扩散模型逐步收紧一致性约束，ECTSpeech在显著降低训练复杂度的同时，实现了高质量的单步生成。此外，我们设计了一个多尺度门控模块（MSGate），以增强降噪器融合不同尺度特征的能力。在LJSpeech数据集上的实验结果表明，ECTSpeech在单步采样下达到了与最先进方法相当的音频质量，同时大幅降低了模型的训练成本和复杂度。
英文摘要：
Diffusion models have demonstrated remarkable performance in speech synthesis, but typically require multi-step sampling, resulting in low inference efficiency. Recent studies address this issue by distilling diffusion models into consistency models, enabling efficient one-step generation. However, these approaches introduce additional training costs and rely heavily on the performance of pre-trained teacher models. In this paper, we propose ECTSpeech, a simple and effective one-step speech synthesis framework that, for the first time, incorporates the Easy Consistency Tuning (ECT) strategy into speech synthesis. By progressively tightening consistency constraints on a pre-trained diffusion model, ECTSpeech achieves high-quality one-step generation while significantly reducing training complexity. In addition, we design a multi-scale gate module (MSGate) to enhance the denoiser's ability to fuse features at different scales. Experimental results on the LJSpeech dataset demonstrate that ECTSpeech achieves audio quality comparable to state-of-the-art methods under single-step sampling, while substantially reducing the model's training cost and complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
