A Lecture Transcription System Combining Neural Network Acoustic And Language Models

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.