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Mobile AR Solution for Deaf People Correlation Between Face Detection and Speech Recognition

机译:聋人面部识别与语音识别相关的移动AR解决方案

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In the last two years, the authors' research has been focused on designing a smart solution to compensate for hearing or visual deficiencies using the Google Glass hardware device and its own software architecture. This paper presents a solution aimed on deaf people or people with hearing impairment. At the beginning of this paper there is a brief explanation of the architecture of the designed solution, a description of the user interface through Google Glass. Related work to face detection, visual activity detection and speech recognition is presented with many possible approaches to these research areas. The principle of the solution lies in the combination of face detection and subsequent assignment of the recognized speech to the mouth of the correct face in the image. The aim of the solution is to digitally capture the ambient sound and based on its evaluation using neural networks, to present detected speech in a text form assigned to detected face from camera stream. The testing has shown that the solution is beneficial, and it is working as expected. Machine Learning Kit provides good results in face detection and communication with Google Cloud Speech API is fast enough for smooth user experience.
机译:在过去的两年中,作者的研究重点是设计一种智能解决方案,以使用Google Glass硬件设备及其自身的软件架构来弥补听觉或视觉缺陷。本文提出了针对聋人或听力障碍者的解决方案。在本文的开头,将对设计的解决方案的体系结构进行简要说明,并通过Google Glass描述用户界面。与面部检测,视觉活动检测和语音识别相关的工作已通过这些研究领域的许多可能方法进行了介绍。该解决方案的原理在于面部检测和随后将识别出的语音分配给图像中正确面部的嘴部的组合。该解决方案的目的是数字捕获环境声音,并基于其使用神经网络的评估结果,以文本形式呈现检测到的语音,该文本形式分配给摄像机流中的检测到的面部。测试表明该解决方案是有益的,并且按预期运行。机器学习工具包在面部检测方面提供了良好的结果,并且与Google Cloud Speech API的通信速度足够快,可以带来流畅的用户体验。

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