自主监督的语音拒绝网络用于多场景的人机交互
Mu Li1, Wenjin Xu1, Chao Zeng2
1Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
这项研究引入了一种新的方法,用于改善噪音环境中的机器人的语音命令识别. 通过使用合成数据和自我监督的无线化网络,该系统在现实应用中实现了更高的准确性.
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
- 语音处理 语音处理
背景情况:
- 使用语音命令的人机交互 (HRI) 已经在视觉语言行动 (VLA) 模型中取得了进展.
- 目前的VLA系统在多音箱场景中与环境噪音和重叠语音作斗争.
- 对于强大的语音命令隔离,需要一个专门的无线化网络.
研究的目的:
- 在杂的环境中增强基于语音命令的HRI.
- 为了提高混合噪音音频的自主监督消噪网络的性能.
- 为了提高语音引导机器人控制的现实世界的适用性.
主要方法:
- 利用合成数据来训练一个自我监督的清除网络.
- 缩放训练数据以提高网络性能,消除混合噪音音频.
- 在具有挑战性的声学条件下开发一种方法来增强语音命令识别.
主要成果:
- 拟议的方法在模拟环境中优于现有的方法.
- 在杂的现实环境中,与最先进的技术相比,获得了7.5%的更高准确度.
- 在实际环境中展示了增强的语音引导机器人控制.
结论:
- 开发的方法有效地提高了在噪音条件下对HRI的语音命令识别.
- 合成数据和自主监督学习对于提高无线网络性能至关重要.
- 该方法为强大的语音引导机器人控制提供了显著的进步.
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