The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The
2014 evaluation offered multiple tracks on lecture transcription and translation based on the …

Recently there has been increasing interest in ways of using out-of-domain (OOD) data to
improve automatic speech recognition performance in domains where only limited data is …

In this paper we investigate techniques to combine hybrid HMM-DNN (hidden Markov model-
deep neural network) and tandem HMM-GMM (hidden Markov model-Gaussian mixture …

We present a fast and scalable online method for tuning statistical machine translation
models with large feature sets. The standard tuning algorithm—MERT—only scales to tens …

In this paper we investigate the use of Multi-level adaptive networks (MLAN) to incorporate
out-of-domain data when training large vocabulary speech recognition systems. In a set of …

In previous work we have introduced a multi-task training technique for neural network
acoustic modelling, in which context-dependent and context-independent targets are jointly …

This paper presents a new system for automatic transcription of lectures. The system
combines a number of novel features, including deep neural network acoustic models using …

N-gram language models are an essential component in statistical natural language
processing systems for tasks such as machine translation, speech recognition, and optical …

This paper describes the University of Edinburgh (UEDIN) ASR systems for the 2014 IWSLT
Evaluation. Notable features of the English system include deep neural network acoustic …

This paper describes the University of Edinburgh (UEDIN) English ASR system for the
IWSLT 2013 Evaluation. Notable features of the system include deep neural network …