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.
Bibliographic reference. Bell, Peter / Yamamoto, Hitoshi / Swietojanski, Pawel / Wu, Youzheng / McInnes, Fergus / Hori, Chiori / Renals, Steve (2013): "A lecture transcription system combining neural network acoustic and language models", In INTERSPEECH-2013, 3087-3091.