深度神经网络的快速调节用于扬声器自适应视觉语音识别
IEEE transactions on pattern analysis and machine intelligence
|October 22, 2024
概括
快速调整通过适应具有最小数据的模型来增强未见的扬声器的视觉语音识别 (VSR). 这种方法可以提高VSR的性能,而不会改变核心深度神经网络的参数.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 语音处理 语音处理
背景情况:
- 视觉语音识别 (VSR) 从唇部运动推断出语音.
- 由于嘴唇外观和运动的变化,VSR模型与看不见的扬声器作斗争.
- 将VSR模型适应新扬声器对于更广泛的适用性至关重要.
研究的目的:
- 使用提示调方法开发适应扬声器的视觉语音识别.
- 为了提高预先训练的VSR模型在隐形扬声器上的性能.
- 调查不同类型的提示符对VSR适应的有效性.
主要方法:
- 建议深度神经网络 (DNN) 在扬声器适应VSR中的提示调整方法.
- 探索了适用于CNN和变压器架构的添加,填充和连接提示形式.
- 精准调整提示针对目标音箱的小适应数据集,避免修改预训练模型参数.
主要成果:
- 在使用最小的适应数据 (不到5分钟) 的隐形扬声器上显著提高了VSR模型的性能.
- 快速调整有效地适应了预先训练的VSR模型,尽管存在扬声器变化.
- 分析提供了有关快速调节与传统微调调节的优势的见解.
结论:
- 快速调提供了一种高效的方法,用于在VSR中适应扬声器.
- 提出的方法在不同的VSR任务和数据集中显示出稳定性和有效性.
- 这种技术显著提高了VSR的可用性,用于不同的,未遇到的扬声器.
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