The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT’16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT’15 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT’14 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM.

This paper describes our English Speech-to-Text (STT) systems for the 2013 IWSLT TED ASR track. The systems consist of multiple subsystems that are combinations of different front-ends, e.g. MVDR-MFCC based and lMel based ones, GMM and NN acoustic models and different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR.

This paper describes our Speech-to-Text (STT) system for French, which was developed as part of our efforts in the Quaero program for the 2013 evaluation. Our STT system consists of six subsystems which were created by combining multiple complementary sources of pronunciation modeling including graphemes with various feature front-ends based on deep neural networks and tonal features. Both speaker-independent and speaker adaptively trained versions of the systems were built. The resulting systems were then combined via confusion network combination and crossadaptation. Through progressive advances and system combination we reach a word error rate (WER) of 16.5% on the 2012 Quaero evaluation data.