The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.
@article{arxiv.1509.03870,
title = {The USFD Spoken Language Translation System for IWSLT 2014},
author = {Raymond W. M. Ng and Mortaza Doulaty and Rama Doddipatla and Wilker Aziz and Kashif Shah and Oscar Saz and Madina Hasan and Ghada AlHarbi and Lucia Specia and Thomas Hain},
journal= {arXiv preprint arXiv:1509.03870},
year = {2015}
}