English(EN) Continuous-Time Acoustic Modelling with Neural Controlled Differential Equations

神经控制微分方程推动文本到语音合成发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-12 04:00

研究人员提出了一种使用神经控制微分方程（CDE）进行文本到语音（TTS）合成的新颖方法。该方法将音素表示建模为连续时间控制路径，使隐藏状态能够根据音素内容和持续时间推导出的时间演变。实验表明，基于CDE的模型可以通过调整时间分辨率来提高情感强度对齐，并提供对风格跟踪与绝对校准的细致控制。 AI

影响通过实现连续时间建模，这项研究可能带来更细致、更富于情感表达的文本到语音系统。

排序理由该集群包含一篇详细介绍语音合成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍语音合成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Mattias Cross, Minghui Zhao, Anton Ragni · 2026-09-12 04:00

基于神经控制微分方程的连续时间声学建模

arXiv:2609.11725v1 Announce Type: cross Abstract: Text-to-speech (TTS) models commonly address text--speech alignment by expanding phone-level encoder states to frame-level decoder inputs using predicted durations. While this length-regulation step resolves alignment structurally…