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Speech-to-Speech Translation pp 53–66Cite as

Language Translation

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Abstract

This chapter introduces the machine translation part. Machine translation (or text-to-text translation) is one component of speech translation and is used for translating automatic speech recognition (ASR) output into text-to-speech (TTS) input. This section will also explain about neural machine translation, which is the most major translation methodology in recent years.

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Notes

  1. 1.

    There is another RNN unit called the gated recurrent unit (GRU). Also, there are several versions of LSTM, which are slightly different from each other.

  2. 2.

    In statistical machine translation, 3- to 5-gram language models are commonly used.

  3. 3.

    When LSTM is used as a RNN unit, it is sometimes called “bi-directional LSTM” or “bi-LSTM.”

  4. 4.

    In contrast to Fig. 4.1, ‘the’ was eliminated.

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Authors and Affiliations

  1. Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology, Kyoto, Japan

    Kenji Imamura

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  1. Kenji Imamura
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Correspondence to Kenji Imamura .

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Editors and Affiliations

  1. Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology, Kyoto, Japan

    Yutaka Kidawara

  2. Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology, Kyoto, Japan

    Eiichiro Sumita

  3. Advanced Speech Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology, Kyoto, Japan

    Hisashi Kawai

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Imamura, K. (2020). Language Translation. In: Kidawara, Y., Sumita, E., Kawai, H. (eds) Speech-to-Speech Translation. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-15-0595-9_4

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  • DOI: https://doi.org/10.1007/978-981-15-0595-9_4

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