Towards Lecture Transcription in Resource-Scarce Environments

Abstract—We present progress towards automated Lecture Transcription (LT) in resource scarce environments. Our devel-opment has focused on the transcription of lectures in Afrikaans from two faculties at North-West University. A bootstrapping procedure is followed to filter and select well-aligned segments of speech. These segments are then used to train acoustic models. Initial work towards language modeling for LT in a resource-scarce environment is also presented; manual lecture transcriptions are combined with text mined from other sources such as study guides to train language models. Interpolation results indicate that study guides are a useful resource for language modeling, whereas general text (obtained from a pub-lisher of Afrikaans books) is less useful in this context. Our findings are confirmed by the reduced word error rates (WERs) obtained from our off-line speech-recognition system for Lecture Transcription