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).
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⟩