跨语言说话人验证自监督预训练研究

1. 12:00
   arXiv · Audio and Speech

   跨语种说话人验证：自监督预训练模型研究

   研究利用大规模自监督预训练模型（PTMs）作为前端特征提取器，学习与语言无关的说话人表征，以解决语言不匹配下的说话人验证性能退化问题。系统在 TidyVoice2026 基准上验证，tv26_eval-A 与 tv26_eval-U 的等错误率（EER）分别为 2.21% 和 2.99%。论文入选 Interspeech2026。

Current heat 5·Comparable peak 10（Oct 9）·Comparable change over 24 hours -50%

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