从视频中提取的面部运动用于语音的动态分类
Richard Palmer1,2, Roslyn Ward2, Petra Helmholz1
1School of Earth and Planetary Sciences, Curtin University, Perth, WA 6102, Australia.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究表明,2D视频中的3D面部测量可以将语音运动分类为诊断语音障碍 (SSD). 这项技术有助于对儿童和成人进行客观评估.
科学领域:
- 语音语言病理学 语言病理学
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 语音声音障碍 (SSD) 对儿童的学术和社会发展产生重大影响.
- 精确诊断SSD对于有效干预和减轻长期影响至关重要.
- 目前用于运动语音控制问题的诊断方法可能是主观的和耗时的.
研究的目的:
- 评估使用自动3D面部测量从2D视频用于分类语音运动的可行性.
- 评估语音运动和声学分析跟踪 (SMAAT) 系统对SSD客观评估的潜力.
- 为了确定从二维视频中推断的深度测量是否可以帮助语音运动分析.
主要方法:
- 收集了51名成人和77名典型发育的儿童 (3-4岁) 发言20个单词的2D正面视频.
- 利用面部网状检测和跟踪算法从视频中提取3D面部测量 (下巴,嘴唇).
- 员工休假一次性交叉验证 (LOOCV) 用于测试单词分类单个测量的性能.
主要成果:
- 几个自动提取的面部测量结果显示,在成人和儿童队伍中,词汇分类的表现显著.
- 从二维视频中推断的深度测量被发现是语音运动的显著预测器.
- 分类测量与预期的面部运动保持一致,验证其临床相关性.
结论:
- 从标准的2D视频中自动化3D面部测量显示了客观语音运动分析的前景.
- 在SSD中,SMAAT系统有潜力作为一种工具,以支持SSD中运动语音控制问题的临床诊断.
- 这种方法提供了一种可行的方法,可以快速客观地评估语音的产生.
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