# End-to-End Spoken Language Translation

arXiv: https://arxiv.org/abs/1904.10760 · HTML: https://mlsanity.com/paper/1904.10760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [A Generative Model for Raw Audio Using Transformer Architectures](https://mlsanity.com/paper/2106.16036/) (2021)
- [Neural Architectures Learning Fourier Transforms, Signal Processing and Much More....](https://mlsanity.com/paper/2308.10388/) (2023)

## Abstract (arXiv)

In this paper, we address the task of spoken language understanding. We present a method for translating spoken sentences from one language into spoken sentences in another language. Given spectrogram-spectrogram pairs, our model can be trained completely from scratch to translate unseen sentences. Our method consists of a pyramidal-bidirectional recurrent network combined with a convolutional network to output sentence-level spectrograms in the target language. Empirically, our model achieves competitive performance with state-of-the-art methods on multiple languages and can generalize to unseen speakers.