了解老年人与智能语音助理的语音交互:一种新的修改后的基于规则的自然语言处理模型,使用人类输入
Zhengxu Yan1, Victoria Dube2, Judith Heselton2
1College of Computing, Data Science and Society, University of California-Berkeley, Berkeley, CA, United States.
Frontiers in digital health
|May 30, 2024
概括
一个新的修改后的基于规则的自然语言处理 (MR-NLP) 模型,增强了人类的输入,有效地分析了老年人的智能语音助理 (SVA) 交互. 这种方法提高了对人工编码的效率和准确性,以了解人口老龄化.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人与计算机的交互 (HCI)
- 老年学是指老年学的学科.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 智能语音助手 (SVA) 的整合在老年人中越来越多,由COVID-19大流行加速.
- 分析来自SVA的用户命令,为老年人的参与模式提供了关键的见解.
- 由于数据量,认知衰退和老年用户的言语改变,手动编码大量语音交互数据具有挑战性.
研究的目的:
- 引入和评估一种新的修改后的基于规则的自然语言处理 (MR-NLP) 模型,用于分析老年人的SVA交互.
- 解决传统文本分析工具在捕捉语音对话的动态性质方面的局限性.
- 加强NLP模型的制定,以了解老龄化人口对人工智能驱动系统的使用.
主要方法:
- 开发一个可重复的MR-NLP模型,将人类获得的洞察力用于关键词词汇和规则制定.
- 在三个月内每天使用亚马逊AlexaTM每天30分钟招募35名老年英语发言者 (平均年龄77岁).
- 在20%的数据子集上,将MR-NLP编码与手动编码进行比较,并调整差异以完善模型.
主要成果:
- 与手动编码相比,MR-NLP方法在效率,可扩展性和减少错误率方面取得了显著的改进.
- 人类的投入对于增强NLP模型至关重要,因为NLP模型具有针对老龄化人口的具体见解,包括语音模式和模两可.
- 精细的MR-NLP框架成功地处理了来自SVA交互的时间标记用户命令和响应.
结论:
- 该MR-NLP模型提供了一个强大的和可扩展的解决方案,用于分析来自老年人的复杂SVA交互数据.
- 整合人类专业知识可以增强NLP模型,从而对特定用户人口统计学产生细微的理解.
- 这种创新的软件解决方案旨在推动NLP研究,并改善针对老龄人口的AI系统设计.
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