REWIND: 用于增强基于扩散的语音转换中说话人表征的语音时间反转方法
REWIND: Speech Time Reversal for Enhancing Speaker Representations in Diffusion-based Voice Conversion
AI总结：
本研究提出REWIND方法，将语音时间反转后的说话人表征作为增强策略，应用于基于扩散的语音转换模型，在保持语音质量的同时显著提升了说话人相似度指标。
摘要：
语音时间反转指的是将整个语音信号在时间维度上反转，使其倒序播放的过程。由于音素和音节的基本结构被破坏，这类信号完全无法被理解。然而，尽管丢失了语言内容，它们仍保留了能够支持感知层面说话人识别的声调模式。本文提出将从时间反转语音中学习到的说话人表征作为一种增强策略，用以提升说话人表征的质量。值得注意的是，语音转换（VC）中的说话人与语言解耦对于准确保留说话人独特的嗓音特征、同时最大限度降低语言内容的干扰至关重要。研究在当前最先进的基于扩散的VC模型场景下评估了所提方法的有效性。实验结果表明，该方法在保持高语音质量的同时，显著提升了与说话人相似度相关的指标得分。
英文摘要：
Speech time reversal refers to the process of reversing the entire speech signal in time, causing it to play backward. Such signals are completely unintelligible since the fundamental structures of phonemes and syllables are destroyed. However, they still retain tonal patterns that enable perceptual speaker identification despite losing linguistic content. In this paper, we propose leveraging speaker representations learned from time reversed speech as an augmentation strategy to enhance speaker representation. Notably, speaker and language disentanglement in voice conversion (VC) is essential to accurately preserve a speaker's unique vocal traits while minimizing interference from linguistic content. The effectiveness of the proposed approach is evaluated in the context of state-of-the-art diffusion-based VC models. Experimental results indicate that the proposed approach significantly improves speaker similarity-related scores while maintaining high speech quality.
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。
