Spoken language translation graphs re-decoding using automatic quality assessment

Abstract : This paper investigates how automatic quality assessment of spoken language translation (SLT), also named confidence estimation (CE), can help re-decoding SLT output graphs and improve the overall speech translation performance. Our graph redecoding method can be seen as a second-pass of translation. For this, a robust word confidence estimator for SLT is required. We propose several estimators based on our estimation of transcription (ASR) quality, translation (MT) quality, or both (combined ASR+MT). Using these word confidence measures to re-decode the spoken language translation graph leads to a significant BLEU improvement (more than 2 points) compared to our SLT baseline, for a French-English SLT task. These results could be applied to interactive speech translation or computer-assisted translation of speeches and lectures.
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Communication dans un congrès
IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Dec 2015, Scotsdale, AZ, United States. 2015, 〈http://www.asru2015.org/〉. 〈10.1109/ASRU.2015.7404804〉
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https://hal.archives-ouvertes.fr/hal-01289158
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Dernière modification le : jeudi 11 octobre 2018 - 08:48:03
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Laurent Besacier, Benjamin Lecouteux, Ngoc-Quang Luong, Ngoc-Tien Le. Spoken language translation graphs re-decoding using automatic quality assessment. IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Dec 2015, Scotsdale, AZ, United States. 2015, 〈http://www.asru2015.org/〉. 〈10.1109/ASRU.2015.7404804〉. 〈hal-01289158〉

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