A lecture transcription system combining neural network acoustic and language models
Peter Bell, H. Yamamoto, Paweł Świętojański, Youzheng Wu, Fergus R. McInnes, Chiori Hori, Steve J. Renals · 2013
This paper presents a new system for automatic transcription of lectures. The system combines a number of novel features, including deep neural network acoustic models using multi-level adaptive networks to incorporate out-of-domain information, and factored recurrent neural network language models. We demonstrate that the system achieves large improvements on the TED lecture transcription task from the 2012 IWSLT evaluation - our results are currently the best reported on this task, showing an relative WER reduction of more than 16% compared to the closest competing system from the evaluation.