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English(EN) RIVET: Robust Idempotent Voice Attribute Editing

RIVET框架通过幂等性目标增强语音编辑的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为RIVET的新训练框架，以提高语音属性编辑模型的鲁棒性。该框架包含一个幂等性目标，确保编辑功能的重复应用产生相同的结果，从而降低对大规模语音数据集中嘈杂或不一致的属性标注的敏感性。在受控标签噪声和GLOBE数据集上的评估表明，与标准训练方法相比，RIVET提高了编辑成功率并更好地保留了说话人身份。 AI

影响 通过解决嘈杂数据问题，提高了语音编辑工具的可靠性。

排序理由 详细介绍语音属性编辑新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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RIVET框架通过幂等性目标增强语音编辑的鲁棒性

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详细介绍语音属性编辑新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dareen Alharthi, Bhuvan Koduru, Rita Singh, Bhiksha Raj ·

    RIVET: Robust Idempotent Voice Attribute Editing

    arXiv:2606.19629v1 Announce Type: cross Abstract: Voice attribute editing models modify characteristics such as age and gender while preserving speaker identity. In large-scale speech datasets, however, attribute annotations are often noisy or inconsistent, which can cause condit…

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