End-to-End Spoken Language Translation

Michelle Guo; Albert Haque; Prateek Verma

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arXiv · 1904.10760

End-to-End Spoken Language Translation

Michelle Guo·Albert Haque·Prateek Verma

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Abstract

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.

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Michelle Guo, Albert Haque, Prateek Verma. 2019-04-23. End-to-End Spoken Language Translation. https://arxiv.org/abs/1904.10760

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