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 the speech recognition systems of IOIT for IWSLT 2014 TED ASR track. This year, we focus on improving acoustic model for the systems using two main approaches of deep neural network which are hybrid and bottleneck feature systems. These two subsystems are combined using lattice Minimum Bayes-Risk decoding. On the 2013 evaluations set, which serves as a progress test set, we were able to reduce the word error rate of our transcription systems from 27.2% to 24.0%, a relative reduction of 11.7%.

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.