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English(EN) Test-time adaptation for speech enhancement with an autoregressive speech prior

新研究探索使用神经音频编解码器进行高级语音增强 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了用于语音增强的高级技术，重点关注在挑战性声学条件下提高音频质量的方法。第一篇论文介绍了一种测试时自适应方法，该方法使用从神经音频编解码器派生的自回归先验来正则化语音增强模型，显示出在训练和测试条件不同时尤其得到改善的质量。第二篇论文提出了一种掩码自回归语音增强方法，该方法利用神经音频编解码器的连续潜在表示，展示了性能和计算成本之间的灵活权衡。 AI

影响 这些论文通过探索神经音频编解码器和自回归模型的新颖用法，推进了语音增强技术，可能在各种应用中带来更高的音频清晰度。

排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了语音增强的新颖方法。

在 arXiv cs.AI 阅读 →

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新研究探索使用神经音频编解码器进行高级语音增强 · 跟踪 2 个来源

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两篇在 arXiv 上发表的学术论文，详细介绍了语音增强的新颖方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sofiene Kammoun, Simon Leglaive, Xavier Alameda-Pineda, Timo Gerkmann ·

    Test-time adaptation for speech enhancement with an autoregressive speech prior

    arXiv:2609.03622v1 Announce Type: cross Abstract: Test-time adaptation (TTA) offers a promising direction for improving speech enhancement models under mismatched acoustic conditions, without requiring access to labeled target data. In this work, we propose a single-utterance TTA…

  2. arXiv cs.AI TIER_1 English(EN) · Yoto Fujita, Simon Leglaive, Laurent Girin ·

    具有连续神经音频编解码器表示的掩码自回归语音增强

    arXiv:2609.03940v1 Announce Type: cross Abstract: Most previous work on speech enhancement (SE) based on masked generative modeling relied on discrete token representations of audio signals, obtained using neural audio codecs (NACs). However, a recent study has shown that continu…

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