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English(EN) RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching

RobustSpeechFlow 通过新颖的训练增强文本到语音的准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 RobustSpeechFlow，一种用于增强文本到语音（TTS）系统鲁棒性的新训练策略。该方法使用基于增强的对比流匹配来直接解决单词跳过和重复等常见错误，在没有外部对齐器的情况下提高了内容保真度。该方法在既定基准上显著降低了单词和字符错误率，从而实现了更准确、更清晰的语音合成。 AI

影响 通过减少单词跳过和重复等常见错误来提高文本到语音的准确性。

排序理由 该集群包含一篇详细介绍文本到语音系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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RobustSpeechFlow 通过新颖的训练增强文本到语音的准确性

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该集群包含一篇详细介绍文本到语音系统新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jinhyeok Yang, Hyeongju Kim, Yechan Yu, Joon Byun, Frederik Bous, Juheon Lee ·

    RobustSpeechFlow：通过基于增强的对比流匹配学习鲁棒的文本到语音轨迹

    arXiv:2605.22083v1 Announce Type: cross Abstract: While flow-matching text-to-speech (TTS) achieves strong zero-shot speaker similarity and naturalness, it remains susceptible to content fidelity issues, particularly skip and repeat errors from imperfect alignment. We propose Rob…

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