Mark Sinclair


2014

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The UEDIN ASR systems for the IWSLT 2014 evaluation
Peter Bell | Pawel Swietojanski | Joris Driesen | Mark Sinclair | Fergus McInnes | Steve Renals
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

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 models in both tandem and hybrid configuration with the use of multi-level adaptive networks, LHUC adaptation and Maxout units. The German system includes lightly supervised training and a new method for dictionary generation. Our voice activity detection system now uses a semi-Markov model to incorporate a prior on utterance lengths. There are improvements of up to 30% relative WER on the tst2013 English test set.

2013

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Description of the UEDIN system for German ASR
Joris Driesen | Peter Bell | Mark Sinclair | Steve Renals
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper we describe the ASR system for German built at the University of Edinburgh (UEDIN) for the 2013 IWSLT evaluation campaign. For ASR, the major challenge to overcome, was to find suitable acoustic training data. Due to the lack of expertly transcribed acoustic speech data for German, acoustic model training had to be performed on publicly available data crawled from the internet. For evaluation, lack of a manual segmentation into utterances was handled in two different ways: by generating an automatic segmentation, and by treating entire input files as a single segment. Demonstrating the latter method is superior in the current task, we obtained a WER of 28.16% on the dev set and 36.21% on the test set.

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The UEDIN English ASR system for the IWSLT 2013 evaluation
Peter Bell | Fergus McInnes | Siva Reddy Gangireddy | Mark Sinclair | Alexandra Birch | Steve Renals
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

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 acoustic models in both tandem and hybrid configuration, cross-domain adaptation with multi-level adaptive networks, and the use of a recurrent neural network language model. Improvements to our system since the 2012 evaluation – which include the use of a significantly improved n-gram language model – result in a 19% relative WER reduction on the tst2012 set.