Where are the challenges in speaker diarization?

Mark Sinclair, Simon King

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Abstract:
We present a study on the contributions to Diarization Error Rate by the various components of speaker diarization system. Following on from an earlier study by Huijbregts and Wooters, we extend into more areas and draw somewhat different conclusions. From a series of experiments combining real, oracle and ideal system components, we are able to conclude that the primary cause of error in diarization is the training of speaker models on impure data, something that is in fact done in every current system. We conclude by suggesting ways to improve future systems, including a focus on training the speaker models from smaller quantities of pure data instead of all the data, as is currently done.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, May 26-31, 2013
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages7741-7745
Number of pages5
ISBN (Print)978-1-4799-0356-6
DOIs
Publication statusPublished - 21 Oct 2013

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