Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.