在对话转录中模拟单词的重要性:为聋人和听力障碍的观众改进现场字幕
Akhter Al Amin1, Matt Huenerfauth2, Saad Hassan1
1Rochester Institute of Technology, Rochester, New York, USA.
... International web for all conference. Web for All Conference
|December 15, 2025
概括
在自动语音识别 (ASR) 字幕中对单词的重要性进行分类,可以改善聋和听障 (DHH) 读者的质量指标. 我们的增强模型显著优于现场标题的先前方法.
科学领域:
- 语音和语言处理 语言处理
- 人与计算机的交互
- 可访问性技术 技术可访问性
背景情况:
- 自动语音识别 (ASR) 系统在实时对话的准确性方面存在局限性.
- 当前的评估指标可能无法完全捕捉聋和听力障碍 (DHH) 读者所经历的标题质量.
- 单词重要性分类是改善标题质量评估的一个有希望的方法.
研究的目的:
- 分析人类注释的单词重要性数据集,用于对话记录.
- 探索部分语音 (POS) 标签和单词重要性之间的关系.
- 开发和评估监督模型,用于预测ASR标题中的单词重要性.
主要方法:
- 在人类注释的数据集上进行了词令牌级别的分析.
- 探索了与单词重要性相关的部分语音 (POS) 分布.
- 通过POS标签增强了数据集,并使用文本生成解决了类不平衡.
- 研究了各种监督学习模型来预测单词的重要性.
主要成果:
- 在增强数据集上训练的表现最好的模型,与之前的模型相比,实现了更高的性能.
- 分析揭示了监督模型如何学习单词的重要性的见解.
- 这些发现支持使用POS标签和数据增强来改进单词重要性分类.
结论:
- 开发的方法可以为现场标题质量的新型评估指标的设计提供信息.
- 这项工作增强了DHH用户在评估标题质量的角度.
- 改进的单词重要性分类有助于为DHH个人提供更有效的ASR系统.
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