Name
Abstract | ||
|---|---|---|
A novel approach to the Bayesian Information Criterion (BIC) is introduced. The new criterion redefines the penalty terms of the BIG, such that each parameter is penalized with the effective sample size is trained with. Contrary to Local-BIG, the proposed criterion scores overall clustering hypotheses and therefore is not restricted to hierarchical clustering algorithms. Contrary to Global-BIC, it provides a local dissimilarity measure that depends only the statistics of the examined clusters and not on the overall sample size. We tested our criterion with two benchmark tests and found significant improvement in performance in the speaker diarisation task. |
Year | Venue | Keywords |
|---|---|---|
2009 | INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | Bayesian Information Criterion, Cluster Analysis, Speaker Diarisation |
Field | DocType | Citations |
Hierarchical clustering,Lemmatisation,Bayesian information criterion,Pattern recognition,Computer science,Computational linguistics,Speech recognition,Information extraction,Speaker diarisation,Artificial intelligence,Cluster analysis,Sample size determination | Conference | 3 |
PageRank | References | Authors |
0.44 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Themos Stafylakis | 1 | 431 | 30.12 |
| Vassilios Katsouros | 2 | 73 | 10.63 |
| George Carayannis | 3 | 215 | 38.14 |