Automatic transcription of academic lectures from diverse disciplines

In a multimedia world it is now common to record professional presentations, on video or with audio only. Such recordings include talks and academic lectures, which are becoming a valuable resource for students and professionals alike. However, organising such material from a diverse set of disciplines seems to be not an easy task. One way to address this problem is to build an Automatic Speech Recognition (ASR) system in order to use its output for analysing such materials. In this work ASR results for lectures from diverse sources are presented. The work is based on a new collection of data, obtained by the Liberated Learning Consortium (LLC). The study's primary goals are two-fold: first to show variability across disciplines from an ASR perspective, and how to choose sources for the construction of language models (LMs); second, to provide an analysis of the lecture transcription for automatic determination of structures in lecture discourse. In particular, we investigate whether there are properties common to lectures from different disciplines. This study focuses on textual features. Lectures are multimodal experiences - it is not clear whether textual features alone are sufficient for the recognition of such common elements, or other features, e.g. acoustic features such as the speaking rate, are needed. The results show that such common properties are retained across disciplines even on ASR output with a Word Error Rate (WER) of 30%.