The relation of hubs to the Doddington zoo in speaker verification

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.