Automatic indexing of lecture speech by extracting topic-independent discourse markers

Automatic detection of section (sub-topic) boundaries in lecture speech is addressed. The method makes use of the characteristic expressions used in initial utterances of sections defined as discourse makers, as well as pause and language model information. The discourse markers are derived in a totally unsupervised manner based on word statistics used in the information retrieval technique. The statistics is used to select candidates picked up by other information. Experimental results show that the proposed method realizes better indexing performance (better precision at high recall rates) than the simple baseline method using pause information only. Moreover, it is shown to be robust against speech recognition errors.