自动预测儿童样本中对自闭症进行丰富的感知对话质量.
Yahan Yang1, Sunghye Cho1, Maxine Covello2
1University of Pennsylvania, Philadelphia, PA.
Interspeech
|July 26, 2024
概括
这项研究表明,分析儿童的对话可以自动预测社会互动的质量. 这项技术可以帮助监测进展,并确定有沟通困难的儿童的目标.
科学领域:
- 计算语言学计算语言学
- 发展心理学是发展心理学.
- 语音语言病理学 语言病理学
背景情况:
- 社会互动的质量对于儿童的发展至关重要.
- 评估儿童的社交沟通,特别是那些患有自闭症谱系障碍 (ASD) 的儿童,可能具有挑战性.
- 需要客观,可扩展的方法来评估社交互动技能.
研究的目的:
- 开发和评估基于自然对话的自动化方法来预测儿童的社交互动质量.
- 为了比较机器学习分类器与人类评分器在评估对话质量的表现.
- 确定有助于预测沟通成功的关键特征.
主要方法:
- 收集了儿童和不熟悉的成年人之间的自然对话.
- 从对话中提取了手工制作的声学和词汇特征.
- 利用机器学习分类器来预测六个维度的社交互动质量.
- 使用预训练的音频变压器提取先进的声学特征.
主要成果:
- 最好的分类器在预测对话质量方面取得了61%的准确性,超过了人类评分器 (49%).
- 声学和词汇特征的一个子集被确定为预测通信质量的关键.
- 结合预训练的音频变压器的功能,预测准确度提高到68%.
结论:
- 谈话质量的自动预测提供了一种廉价且客观的方法来监测儿童的干预进展.
- 这项技术可以帮助确定具体的干预目标,以提高有沟通困难的儿童的对话成功率.
- 这些发现支持AI驱动工具在发育和临床评估中的潜力.
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