将 TTS 的语言表示迁移至基于非平行数据的口音转换
Transfer the linguistic representations from TTS to accent conversion with non-parallel data
AI总结：
本文提出一种基于非平行数据的非自回归口音转换框架，通过学习口音无关的语言表示并与 TTS 系统的语言表示对齐，结合母语数据预训练策略与丰富语义特征，显著提升了转换语音的质量与可懂度。
摘要：
口音转换旨在将源语音的口音转换为目标口音，同时保留说话人身份。本文提出了一种新颖的用于口音转换的非自回归框架，该框架学习口音无关的语言表示，并利用其在源语音中进行口音转换。具体而言，所提出的系统将语音表示与从 Text-to-Speech (TTS) 系统获得的语言表示进行对齐，从而能够在非平行数据上训练口音语音转换模型。此外，我们研究了在所提出框架内对母语数据进行预训练策略的有效性以及不同声学特征的作用。我们使用主观和客观指标进行了全面评估，以评估本方法的性能。评估结果突出了预训练策略和引入更丰富语义特征的优势，从而显著提升了音频质量与可懂度。
英文摘要：
Accent conversion aims to convert the accent of a source speech to a target accent, meanwhile preserving the speaker's identity. This paper introduces a novel non-autoregressive framework for accent conversion that learns accent-agnostic linguistic representations and employs them to convert the accent in the source speech. Specifically, the proposed system aligns speech representations with linguistic representations obtained from Text-to-Speech (TTS) systems, enabling training of the accent voice conversion model on non-parallel data. Furthermore, we investigate the effectiveness of a pretraining strategy on native data and different acoustic features within our proposed framework. We conduct a comprehensive evaluation using both subjective and objective metrics to assess the performance of our approach. The evaluation results highlight the benefits of the pretraining strategy and the incorporation of richer semantic features, resulting in significantly enhanced audio quality and intelligibility.
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。
