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解码咳声音中的性别:基于变压器的分析

Linh He1, Haomiao Li2, Siyan Wang2

  • 1Sean Parker Institute for the Voice, Department of Otolaryngology - Head & Neck Surgery, Weill Cornell Medical College, New York, New York, USA.

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|January 27, 2026
PubMed
概括
此摘要是机器生成的。

咳声学包含性别特征,可通过AI模型检测. 这些发现可以通过识别非语音声音差异来帮助性别肯定语音护理.

关键词:
在这里,我们可以看到AIAIAI.咳 咳 咳 咳 咳 咳 咳 咳性别 性别 性别 性别 性别

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科学领域:

  • 喉腔病理学 喉腔病理学
  • 声学分析 声学分析
  • 人工智能的人工智能

背景情况:

  • 音调和音量等语音特征会影响性别认知.
  • 关于性别差异,非语音声音,例如咳,对性别差异的理解较少.
  • 这种知识差距影响了性别确认的语音护理,因为咳很难调节.

研究的目的:

  • 为了研究咳声音的基于性别的声学差异.
  • 使用具有自我注意力的变压器模型来识别用于性别分类的关键咳特征.

主要方法:

  • 来自Coswara数据集的327个咳记录 (154名男性,173名女性) 的分析.
  • 应用HuBERT变压器模型用于预处理后的音频分析 (重新采样,消除沉默,规范化,修剪).
  • 使用标准化平均差异 (SMD) 查验证性别平衡.

主要成果:

  • 变压器模型在从咳系列中分类性别时,获得了84.0%的准确率和0.8462的F1得分.
  • 仅使用单一咳样本时,分类准确性较低 (71.4%).
  • 注意力可视化强调爆炸性咳阶段对于性别区分至关重要.

结论:

  • 咳的声音具有性别歧视的声学特征,可通过变压器模型识别.
  • 具体的咳阶段包含了生理学上与性别分类相关的信息.
  • 调查结果可以为非语音声音制作的性别肯定干预提供信息.