The RWTH English lecture recognition system

In this paper, we describe the RWTH speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high ratio of non-native speakers. We address this problem by using very effective deep bottleneck features trained on multilingual data. The acoustic model is trained on large amounts of data from different domains and with different dialects. Large improvements are obtained from unsupervised acoustic adaptation. Another challenge is the frequent use of technical terms and the wide range of topics. In our recognition system, slides, which are attached to most lectures, are used for improving lexical coverage and language model adaptation.