使用生成性人工智能重建语言障碍,用于阿法西亚患者
Achini Adikari1, Damminda Alahakoon2, Nuwan Pallewela2
1Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Melbourne, Australia. A.Adikari@latrobe.edu.au.
Scientific reports
|November 19, 2025
概括
生成型人工智能 (AI) 和大型语言模型 (LLM) 现在可以通过纠正实时对话中的语音错误来帮助获得沟通障碍的成年人. 这种由人工智能驱动的解决方案在重建失语语音的准确性达到80%.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
- 语音和语言病理学 语言病理学
背景情况:
- 生成型人工智能 (AI) 和大型语言模型 (LLM) 提供了帮助语言障碍患者的潜力.
- 现有的关于失言症LLM的研究往往集中在有限的任务上,缺乏现实世界的对话可靠性.
- 获得的沟通障碍,如失语症,显著影响日常互动.
研究的目的:
- 开发和评估一种人工智能驱动的语言辅助解决方案,用于在自然对话中为患有失言症的人提供语言辅助.
- 利用LLM来检测和纠正语音错误,包括新词语,转相错误和查词困难.
- 增强成年人与获得的沟通障碍的对话流性和连贯性.
主要方法:
- 使用大型语言模型 (LLM),在没有模型重量更新的情况下,对错误纠正进行上下文几次提示.
- 将LLM集成到使用Langchain架构的对话系统中,以保持对话上下文.
- 训练和测试了该系统的数据集约1980发言从180名参与者在AphasiaBank的语音库.
主要成果:
- 使用GPT-4o模型,人工智能重建的发言实现了80%的准确率.
- 该系统证明了它能够纠正新词汇,副阶段错误和词查找差距的能力.
- 研究了各种语音错误类型对LLM重建准确性的影响.
结论:
- 基于LLM的对话系统显示出对阿法西亚患者实时语言辅助的重大前景.
- 与特定任务的LLM应用程序相比,开发的方法为对话支持提供了更可靠的解决方案.
- 进一步的研究可以通过分析错误类型来完善系统,以提高语音障碍的纠正能力.
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