We present a framework for building speech-to-text translation (ST) systems
using only monolingual speech and text corpora, in other words, speech
utterances from a source language and independent text from a target language.
As opposed to traditional cascaded systems and end-to-end architectures, our
system does not require any labeled data (i.e., transcribed source audio or
parallel source and target text corpora) during training, making it especially
applicable to language pairs with very few or even zero bilingual resources.
The framework initializes the ST system with a cross-modal bilingual dictionary
inferred from the monolingual corpora, that maps every source speech segment
corresponding to a spoken word to its target text translation. For unseen
source speech utterances, the system first performs word-by-word translation on
each speech segment in the utterance. The translation is improved by leveraging
a language model and a sequence denoising autoencoder to provide prior
knowledge about the target language. Experimental results show that our
unsupervised system achieves comparable BLEU scores to supervised end-to-end
models despite the lack of supervision. We also provide an ablation analysis to
examine the utility of each component in our system.

本文提出了一种基于单语音频和文本语料库的语音翻译系统框架，使用交叉模态双语词典将每个源语音片段对应到目标文本翻译，通过使用语言模型和序列去噪自编码器，对每个音频片段进行逐字翻译来提高翻译质量，实验结果表明，我们的无监督系统尽管没有监督，但达到了与监督方法相比可比的 BLEU 分数。