The Microsoft 2017 Conversational Speech Recognition System
W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke . 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017 – 313 citations
We describe the 2017 version of Microsoft’s conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1% word error rate on the 2000 Switchboard evaluation set.