Deep Learning methods employ multiple processing layers to learn hierarchial representations of data. They have already been deployed in a humongous number of applications and have produced state-of-the-art results. Recently with the growth in processing power of computers to be able to do high dimensional tensor calculations, Natural Language Processing (NLP) applications have been given a significant boost in terms of efficiency as well as accuracy. In this paper, we will take a look at various signal processing techniques and then application of them to produce a speech-to-text system using Deep Recurrent Neural Networks.
@article{arxiv.1808.04459,
title = {Deep Learning Based Natural Language Processing for End to End Speech Translation},
author = {Sarvesh Patil},
journal= {arXiv preprint arXiv:1808.04459},
year = {2018}
}