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English(EN) TokAN: Accent Normalization Using Self-Supervised Speech Tokens

TokAN框架使用自监督语音令牌进行口音归一化

研究人员开发了TokAN,一个新颖的口音归一化框架,可将非母语口音转换为标准口音,同时保留说话人身份。与需要并行L1-L2语音数据或在合成目标时出现质量下降的先前方法不同,TokAN利用自监督离散语音令牌。该系统采用自回归编码器-解码器模型进行令牌到令牌的转换,并结合强化学习进行训练后优化,进一步降低了词错误率。在七种英语口音上的实验表明,TokAN在口音减少和可懂度方面显著优于现有基线。 AI

影响 这项研究推进了语音处理能力,可能提高了语音技术对非母语使用者的可访问性和可用性。

排序理由 详细介绍一种新的口音归一化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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TokAN框架使用自监督语音令牌进行口音归一化

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详细介绍一种新的口音归一化方法的论文。[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) · Qibing Bai, Shuai Wang, Yuhan Du, Bohan Li, Yannan Wang, Haizhou Li ·

    TokAN:使用自监督语音令牌进行口音规范化

    arXiv:2607.03928v1 Announce Type: cross Abstract: Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity. The current techniques either require naturally recorded parallel L1-L2 speech for training, o…