Auto-Adaptive Learning for Machine Perception of Native Accent Using Deep Learning

One of the solutions to artificial intelligence is machine learning. It enables us to create machines that can learn from experience rather than be programmed explicitly. Current formulations of machine learning are mostly designed with the help of data available for learning and performing specific tasks from neural networks. Deep learning is an effective machine learning approach that can solve multiple and specific tasks with mini mother change. Deep learning extends machine learning to multi-level distributed representations with the necessary mapping functions into a single composite function, and in particular neural networks. Along with their capability to learn dynamic hierarchical representations, the advent of deep learning and neural networks has opened up the way for continuous training. The main objective of this thesis is to research and establish a systematic approach to continuous learning that facilitates the success of profound education and neural networks.