ECTSpeech: Enhancing Efficient Speech Synthesis via Easy Consistency Tuning

• URL: http://arxiv.org/abs/2510.05984v1
• Date: Tue, 07 Oct 2025 14:44:05 GMT
• Title: ECTSpeech: Enhancing Efficient Speech Synthesis via Easy Consistency Tuning
• Authors: Tao Zhu, Yinfeng Yu, Liejun Wang, Fuchun Sun, Wendong Zheng,
• Abstract summary: We propose ECTSpeech, a simple and effective one-step synthesis framework.<n>ECTSpeech incorporates the Easy Consistency Tuning (ECT) strategy into speech synthesis.<n>We show that ECTSpeech achieves audio quality comparable to state-of-the-art methods under single-step sampling.
• Score: 37.55301116117562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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