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Stereotype Content Model

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...

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Updated: Jul 23, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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基于语音识别的辅助机器人的高效自我注意模型控制控制

Samuel Poirier^1,2, Ulysse Côté-Allard³, François Routhier^1,2

¹Université Laval, Quebec City, QC G1V 0A6, Canada.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

这项研究引入了一种轻量级的语音命令识别系统,用于辅助机器人,改善了对身体上部残疾人的控制. 这种新的系统在语音指令数据集上实现了最先进的性能,并展示了现实世界的机器人手臂控制.

关键词:

有助于机器人的机器人深度学习是一种深度学习.人机界面人机界面关键词发现关键词发现机器人辅助手臂是一种机器人辅助手臂.自己注意力自我注意力语音指令指令语言指令指令语音识别语音识别语言识别转移学习转移学习

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Last Updated: Jul 23, 2025

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科学领域:

机器人技术机器人技术机器人技术
人工智能的人工智能
人与计算机的交互

背景情况:

常规的辅助机器人接口,如操纵杆,对于身体上部残疾的用户来说往往是不直观的.
语音命令为控制辅助机器人和执行日常生活活动 (ADL) 提供了更自然和更容易获得的替代方案.

研究的目的:

开发一种新的,轻量级的语音命令识别系统,用于辅助机器人.
通过直观的语音接口,为上肢残疾人增强辅助机器人的可用性.

主要方法:

开发了一个新的轻量级语音命令识别系统,利用MobileNet2架构和一个新的自我注意力机制.
该系统在谷歌语音命令数据集 (GSCD) 中实现了关键字发现 (KWS) 的最先进的性能.
使用转移学习 (TL) 将模型调整为新的法语语音命令数据集 (FSCD),证明了跨语言的有效性.

主要成果:

拟议的模型在谷歌语音命令数据集 (GSCD) 上实现了最先进的关键词识别 (KWS) 性能.
转移学习 (TL) 显著改善了该模型在跨语言法语语音命令数据集 (FSCD) 上的性能.
现实生活中的机器人手臂控制通过健康参与者使用语音接口成功演示.

结论:

开发的轻量级语音命令识别系统为辅助机器人提供了可行的和直观的界面.
这种新的方法和法国语音命令数据集 (FSCD) 推动了语音控制辅助技术领域的发展.
这项技术具有显著的潜力,可以提高上肢残疾人的自主性和生活质量.