The NDSC transcription system for the 2016 multi-genre broadcast challenge

Abstract

The National Digital Switching System Engineering and Technological R&D Center (NDSC) speech-to-text transcription system for the 2016 multi-genre broadcast challenge is described. Various acoustic models based on deep neural network (DNN), such as hybrid DNN, long short term memory recurrent neural network (LSTM RNN), and time delay neural network (TDNN), are trained. The system also makes use of recurrent neural network language models (RNNLMs) for re-scoring and minimum Bayes risk (MBR) combination. The WER on test dataset of the speech-to-text task is 18.2%. Furthermore, to simulate real applications where manual segmentations were not available an automatic segmentation system based on long-term information is proposed. WERs based on the automatically generated segments were slightly worse than that based on the manual segmentations.