8 research outputs found
FBK @ IWSLT 2014 - ASR track
This paper reports on the participation of FBK in the IWSLT 2014
evaluation campaign for Automatic Speech Recognition (ASR), which
focused on the transcription of TED talks. The outputs of primary and
contrastive systems were submitted for three languages, namely
English, German and Italian.
Most effort went into the development of the English transcription
system. The primary system is based on the ROVER combination of the
output of 5 transcription sub-systems which are all based on the Deep
Neural Network - Hidden Markov Model (DNN-HMM) hybrid. Before
combination, word lattices generated by each sub-system are rescored
using an efficient interpolation of 4-gram and Recurrent Neural
Network (RNN) language models. The primary system achieves a Word
Error Rate (WER) of 14.7% and 11.4% on the 2013 and 2014
official IWSLT English test sets, respectively.
The subspace Gaussian mixture model (SGMM) system developed for
German achieves 39.5% WER on the 2014 IWSLT
German test sets. For Italian, the primary
transcription system was based on hidden Markov models and achieves
23.8% WER on the 2014 IWSLT Italian test set