通过TinyML赋权语音助理,以实现以用户为中心的创新和现实世界的应用
Sireesha Chittepu1, Sheshikala Martha1, Debajyoty Banik2
1School of CS & AI, SR University, Warangal, India.
Scientific reports
|May 2, 2025
概括
本研究探讨了基于微型机器学习 (TinyML) 的语音助理,以在智能家居和辅助技术中提供更好的用户体验. 它确定了改善功能和用户参与的挑战和研究差距.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 嵌入式系统 嵌入式系统
背景情况:
- 微型机器学习 (TinyML) 能够实现设备内人工智能处理,这对于资源有限的应用程序至关重要.
- 语音助理提供了直观的交互,但在现实世界部署和用户适应方面面临挑战.
- 将TinyML集成到语音助理中,有望增强用户界面,功能和个性化用户体验.
研究的目的:
- 探索将基于TinyML的语音助理集成到日常生活中的动机.
- 识别和分析当前TinyML语音助理实现中的局限性和研究差距.
- 突出动态用户参与的潜在应用和解决方案.
主要方法:
- 对语音助理中的TinyML应用进行了全面的文献审查.
- 分析现实世界的使用案例,包括智能家居,视力障碍者辅助技术和医疗保健.
- 对现有方法进行比较审查,以确定研究缺口.
主要成果:
- TinyML语音助理在智能家居,辅助技术和医疗监控方面提供了巨大的潜力.
- 主要挑战包括部署困难,噪音干扰和低资源设备上的模型效率.
- 了解用户行为模式可以实现动态内容交付和增强用户参与度.
结论:
- TinyML集成对于推进语音助理功能和用户体验至关重要.
- 解决发现的研究缺口,如部署和效率,对于更广泛的采用至关重要.
- 这项研究为研究人员提供了洞察力,让他们专注于TinyML语音助理开发中的开放问题.
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