This paper describes the Automatic Speech Recognition (ASR) and Machine Translation (MT) systems developed by IOIT for the evaluation campaign of IWSLT2013. For the ASR task, using Kaldi toolkit, we developed the system based on weighted finite state transducer. The system is constructed by applying several techniques, notably, subspace Gaussian mixture models, speaker adaptation, discriminative training, system combination and SOUL, a neural network language model. The techniques used for automatic segmentation are also clarified. Besides, we compared different types of SOUL models in order to study the impact of words of previous sentences in predicting words in language modeling. For the MT task, the baseline system was built based on the open source toolkit N-code, then being augmented by using SOUL on top, i.e., in N-best rescoring phase.

LIMSI took part in the IWSLT 2011 TED task in the MT track for English to French using the in-house n-code system, which implements the n-gram based approach to Machine Translation. This framework not only allows to achieve state-of-the-art results for this language pair, but is also appealing due to its conceptual simplicity and its use of well understood statistical language models. Using this approach, we compare several ways to adapt our existing systems and resources to the TED task with mixture of language models and try to provide an analysis of the modest gains obtained by training a log linear combination of inand out-of-domain models.