基于二次动态时间翘曲的自主机械臂目标识别与实时语音识别

Irfan Al-Hussaini, Zubayer Islam, Antik Mallick, M. E. Hoque
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引用次数: 1

摘要

本文提出了一种基于实时语音和图像的自主机械臂指令处理机制。我们提出了一种新的两阶段语音识别算法,每个阶段使用两次动态时间扭曲。实时唤醒词识别之后,使用k-means进行离线命令识别。由于高精度在任何控制系统激活机制中都是至关重要的,因此设置限制性阈值以获得1的精度。这就缓解了控制系统的意外触发问题。目标识别和分类是通过匹配由局部特征检测器和描述符产生的特征来完成的。该算法控制一个具有5个自由度的机械臂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object recognition and real-time spoken word recognition using two-fold dynamic time warping for autonomous arm manipulator
In this paper, we propound a command processing mechanism for an autonomous arm manipulator using real-time speech and images. We propose a novel two-stage speech recognition algorithm using two-fold Dynamic Time Warping in each stage. Real-time wake-up word recognition is followed by offline command recognition using k-means. Since high precision is paramount in any control system activation mechanism, a restrictive threshold is set to gain a precision of 1. This alleviates the problem of accidental triggering of the control system. Object recognition and classification is performed by matching features resulting from a local feature detector and descriptor. The algorithm controls an arm manipulator with 5 degrees of freedom.
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