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.

Russian is a challenging language for automatic speech recognition systems due to its rich morphology. This rich morphology stems from Russian’s highly inflectional nature and the frequent use of preand suffixes. Also, Russian has a very free word order, changes in which are used to reflect connotations of the sentences. Dealing with these phenomena is rather difficult for traditional n-gram models. We therefore investigate in this paper the use of a maximum entropy language model for Russian whose features are specifically designed to deal with the inflections in Russian, as well as the loose word order. We combine this with a subword based language model in order to alleviate the problem of large vocabulary sizes necessary for dealing with highly inflecting languages. Applying the maximum entropy language model during re-scoring improves the word error rate of our recognition system by 1.2% absolute, while the use of the sub-word based language model reduces the vocabulary size from 120k to 40k and the OOV rate from 4.8% to 2.1%.