An Artificial Intelligence-based Application for Recognizing and Identifying Aerial Objects based on Voice Input

Abstract

Visual observation to recognize and identify aerial objects is a means to protect air sovereignty, and the delay can endanger it. Visual observation can be done through Ground-to-Air (GTA) or by approaching through Air-to-Air (ATA) using binoculars. The observation needs accuracy and speed to speed up the decision-making process. We propose an Artificial Intelligence (AI) system that receives voice input to recognize and identify an Unmanned Aerial Aircraft (UAA). We employed Naïve Bayes Classifier (NBC) that processes inputs from voice-to-text tools containing the observed UAA's feature. With 70 UAA samples, the AI system achieved an accuracy of 79% and a WER of 4.16%.