People who are not experts in robotics can easily implement complex robotic applications by using human–robot interaction (HRI). HRI systems require many complex operations such as robot control, image processing, natural speech recognition, and decision making. In this study, interactive control with an industrial robot was performed by using speech recognition software in the Turkish language. The collected voice data were converted to text data by using automatic speech recognition module based on deep neural networks (DNN). The proposed DNN (p-DNN) was compared to classic classification algorithms. Converted text data was improved in another module to select the process to be applied. According to selected process, position data were defined using image processing. The determined position information was sent to the robot using a fuzzy controller. The developed HRI system was implemented on a KUKA KR Agilus KR6 R900 sixx robot manipulator. The word accuracy rate of the p-DNN model was measured as 90.37%. The developed image processing module and fuzzy controller worked with minimal errors. The contribution of this study is that an industrial robot is easily programming using this software by people who are not experts in robotics and know Turkish.

不是机器人技术专家的人可以使用人机交互（HRI）轻松实现复杂的机器人应用。HRI系统需要许多复杂的操作，例如机器人控制，图像处理，自然语音识别和决策。在这项研究中，使用土耳其语语音识别软件执行了与工业机器人的交互式控制。使用基于深度神经网络（DNN）的自动语音识别模块将收集的语音数据转换为文本数据。提议的DNN（p-DNN）与经典分类算法进行了比较。在另一个模块中改进了转换后的文本数据，以选择要应用的过程。根据选择的过程，使用图像处理定义位置数据。使用模糊控制器将确定的位置信息发送到机器人。开发的HRI系统在KUKA KR Agilus KR6 R900 sixx机器人操纵器上实施。p-DNN模型的单词准确率测得为90.37％。所开发的图像处理模块和模糊控制器能够以最小的误差工作。这项研究的贡献在于，工业机器人可以由非机器人专家和土耳其语的人轻松地使用此软件进行编程。