# Deep Learning Based Natural Language Processing for End to End Speech Translation

arXiv: https://arxiv.org/abs/1808.04459 · HTML: https://mlsanity.com/paper/1808.04459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs](https://mlsanity.com/paper/2605.24681/) (2026)

## Abstract (arXiv)

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.