This paper describes the speech recognition system of IOIT for IWSLT 2016. Four single DNN-based systems were developed to produce the 1st-pass lattices for the test sets using a baseline language model. The 2nd-pass lattices were further obtained by applying N-best list rescoring on topic adapted language models which were constructed from closed topic sentences by applying a text selection method. The final transcriptions of test sets were finally produced by combining the rescored results. On the 2013 evaluation set, we are able to reduce the word error rate of 1.62% absolute. On the 2014, provided as a development set, the word error rate of our transcription is 11.3%.

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.