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SPARCLE模型提升低资源场景下的文本转语音效果

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出SPARCLE，一种新颖的说话人感知字形表示模型，旨在改进文本转语音（TTS）合成，尤其是在低资源场景下。与依赖字形到音素转换器的传统基于音素的系统不同，SPARCLE直接将字形与声学表示对齐，并融入说话人身份信息。与标准的基于字形的模型相比，该方法在极低资源场景下显著提高了效果，将词错误率降低了一半。 AI

影响 该模型有望显著提高文本转语音系统的质量和可访问性，特别是对于代表性不足的语言或口音。

排序理由 该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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SPARCLE模型提升低资源场景下的文本转语音效果

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该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Priyam Mazumdar, Yurii Halychanskyi, Steven Guo, Mark Hasegawa-Johnson, Volodymyr Kindratenko ·

    SPARCLE：通过对比语言嵌入实现说话人感知的对齐表示

    arXiv:2607.01238v1 Announce Type: cross Abstract: Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapping between text and acoustics, they rely on grapheme-to-phoneme (G2P) systems t…

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