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.

This paper describes the Alborada-I3A corpus of disordered speech, acquired during the recent years for the research in different speech technologies for the handicapped like Automatic Speech Recognition or pronunciation assessment. It contains more than 2 hours of speech from 14 young impaired speakers and nearly 9 hours from 232 unimpaired age-matched peers whose collaboration was possible by the joint work with different educational and assistive institutions. Furthermore, some extra resources are provided with the corpus, including the results of a perceptual human-based labeling of the lexical mispronunciations made by the impaired speakers. The corpus has been used to achieve results in different tasks like analyses on the speech production in impaired children, acoustic and lexical adaptation for ASR and studies on the speech proficiency of the impaired speakers. Finally, the full corpus is freely available for the research community with the only restrictions of maintaining all its data and resources for research purposes only and keeping the privacy of the speakers and their speech data