A Report on Voice Recognition System: Techniques, Methodologies and Challenges using Deep Neural Network

Abstract

Voice recognition has been advancing at a fast rate. Many cases involving edited audio clips and incorrect identity claims are reported on a daily basis. Due to the growing importance of information processing technology, it becomes easier and easier to identify people by their voices. Voice recognition consists of detecting a user's identity based on characteristics of their voice. It is a widely applied form of biometric recognition in the world, particularly in fields where security has a high priority. The deep neural networks were used as feature extractor alongside classifiers, but they haven't been completely trained due to the success of deep learning. While such methods are extremely efficient, they still require manual attention. Especially in DNN, interactivity between people and machines is essential. This is where the art of voice recognition comes from. In addition to their application in speech recognition, deep neural networks have demonstrated their potential to be used for voice recognition as well. They provide an efficient implementation of complex nonlinear models for learning unique and invariant data structures. The main contribution of this work is to provide a brief overview of the field of deep neural networks and voice recognition, describing its system, underlying approaches, and challenges.