Speech-to-Speech Translation Between Untranscribed Unknown Languages
Speech-to-Speech Translation Between Untranscribed Unknown Languages
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DOI：
10.1109/asru46091.2019.9003853
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Andros Tjandra;S. Sakti;Satoshi Nakamura
中科院分区：
文献类型：
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作者：
Andros Tjandra;S. Sakti;Satoshi Nakamura
在本文中，我们探索了一种在没有任何转录或语言监督的情况下训练语音到语音翻译任务的方法。我们提出的方法包括两个步骤：首先，我们用离散量化自动编码器训练和生成离散表示，并使用无监督的术语发现。其次，我们训练了一个序列到序列模型，该模型直接将源语言语音映射到目标语言的离散表示。我们所提出的方法可以直接生成目标语音，而无需任何辅助或预训练步骤与源或目标转录。据我们所知，这是第一个在未转录的未知语言之间进行纯语音到语音翻译的工作。
In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target languages discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.