Object Detection using Speech Recognition

Abstract

Nearly all practical applications, including autonomous navigation, visual systems, face recognition, and more, rely on object detection. In this paper, object detection and speech recognition are combined to help visually impaired people who want to use voice commands to find a certain object. People who are blind or visually challenged can move more independently if they are aware of their surroundings. With the use of the OpenCV libraries, a model has been implemented, and good results have been obtained. In this paper, a thorough review of object detection employing region-based conventional neural network (CNN)- based learning systems for practical applications has been conducted. This study examines the various object identification processes utilizing YOLOV4 object detection techniques and talks through detection, including a speech recognition system that was created by transcribing spoken language into text.