Analysis of Connected Word Recognition systems using Levenberg Marquardt Algorithm for cockpit control in unmanned aircrafts

Due to advances in computation, the computer system needs sufficient input data, and it allows it a better computer tool for efficient operation of the human-computer, such as the fast-moving Automatic Speech Recognition System. This paper aims in particular to provide an insight into the contact distance between humans and computers in unmanned aircraft vehicles. While there are several algorithms, a critical analysis of algorithms suitable for large-scale applications is still important. The aircraft without a human pilot on board is an unmanned aerial vehicle. Continuous Word Recognition systems for voice enhancement (commanding) based cockpit control are commonly used in unmanned aircraft. The goal is to evaluate the efficiency of the Levenberg Marquardt algorithm by using these recognition systems. To do this, optimal preparation can be selected using neural networks to increase the machine recognition effectiveness. MATLAB verify simulated findings and tests show that a high accuracy of recognition of over 87 percent is obtained. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper focuses on the contact distance between humans and unmanned aircraft vehicles, as well as the efficiency evaluation of the Levenberg Marquardt algorithm in voice recognition systems used in unmanned aircraft. Through critical analysis, it demonstrates the feasibility of achieving high accuracy recognition in unmanned aircraft vehicles.