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Contribution for newspaper or weekly magazine . 2014

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The UEDIN ASR Systems for the IWSLT 2014 Evaluation

descriptionPublicationkeyboard_double_arrow_right Contribution for newspaper or weekly magazine 01 Jan 2014 United Kingdom English Funded by:EC | INEVENT, EC | EU-BRIDGE

Authors: Bell, Peter; Swietojanski, Pawel; Driesen, Joris; Sinclair, Mark; McInnes, Fergus; Renals, Steve;

handle: 20.500.11820/903f2107-febc-47c3-bb68-f6ab43f48af6

The UEDIN ASR Systems for the IWSLT 2014 Evaluation

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Abstract

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.

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University of Edinburgh
United Kingdom

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EC| INEVENT, EC| EU-BRIDGE

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