Spoken Language Translation Graphs Re-decoding using Automatic Quality Assessment

Abstract : This paper investigates how automatic quality assessment of spoken language translation (SLT) can help re-decoding SLT output graphs and improving the overall speech translation performance. Using robust word confidence measures (from both ASR and MT) to re-decode the SLT graph leads to a significant BLEU improvement (more than 2 points) compared to our SLT baseline (French-English task).
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Poster communications
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https://hal.archives-ouvertes.fr/hal-02095256
Contributor : Benjamin Lecouteux <>
Submitted on : Wednesday, April 10, 2019 - 12:11:20 PM
Last modification on : Monday, July 8, 2019 - 3:10:11 PM

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  • HAL Id : hal-02095256, version 1

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Laurent Besacier, Benjamin Lecouteux, Ngoc Luong, Ngoc Le. Spoken Language Translation Graphs Re-decoding using Automatic Quality Assessment. ASRU, 2015, Scotsdale, United States. ⟨hal-02095256⟩

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