THE RELATION OF HUBS TO THE DODDINGTON ZOO IN SPEAKER VERIFICATION

摘要

In speaker verification systems there exists the well-known phenomenon of speakers which are very problematic to verify and have been given various metaphoric animal names. Our work connects this so-called 'Doddington zoo' and the animals of the whole 'biometric menagerie' to the problem of 'hubs' in high dimensional data spaces, which was recently the topic of a number of publications in the machine learning literature. Due to a general problem of measuring distances in high dimensional data spaces, hub objects emerge which have a high similarity to a large number of data items. This is a novel aspect of the 'curse of dimensionality' which adversely affects classification and identification performance. In a series of experiments we try to understand the 'Doddington zoo' problem with respect to the notions of hubs and anti-hubs.