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.
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 language | English |
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| Title of host publication | IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, May 26-31, 2013 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 7741-7745 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4799-0356-6 |
| DOIs | |
| Publication status | Published - 21 Oct 2013 |