The NCT ASR system for IWSLT 2014
Peng Shen | Yugang Lu | Xinhui Hu | Naoyuki Kanda | Masahiro Saiko | Chiori Hori
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign
This paper describes our automatic speech recognition system for IWSLT2014 evaluation campaign. The system is based on weighted finite-state transducers and a combination of multiple subsystems which consists of four types of acoustic feature sets, four types of acoustic models, and N-gram and recurrent neural network language models. Compared with our system used in last year, we added additional subsystems based on deep neural network modeling on filter bank feature and convolutional deep neural network modeling on filter bank feature with tonal features. In addition, modifications and improvements on automatic acoustic segmentation and deep neural network speaker adaptation were applied. Compared with our last year’s system on speech recognition experiments, our new system achieved 21.5% relative improvement on word error rate on the 2013 English test data set.