Cognitive Model for Object Detection based on Speech-to-Text Conversion

Abstract

The goal of this paper is to develop a model which is the integrated version of both SpeechRecognition and Object detection. This model is developed after undergoing the literature survey and the existing models that are related to Object Detection and Speech Recognition. There are several types of Speech Recognition and Object Detection models available so far. In addition to the existing models, this paper proposes a new model named "Cognitive Model for Object Detection based on Speech-to-Text Conversion," which is an integrated version of both Speech Recognition and Object Detection models. Firstly, A speech command is provided as an input to the model, it takes the command and processes the data, and then it detects the specified object from a source of images. The detected object is represented with a rectangular box. This approach is implemented with the help of Google Speech Recognition and YOLO object detection models utilizing the Darknet and OpenCV frameworks.