This paper investigates an unsupervised speaker clustering ap-proach that 
exploits global similarity and also proposes extend-ing the standard cepstal 
feature set used for speaker clustering with prosodic features, extracted from F0. 
The global-similarity based speaker clustering algorithm, initially proposed 
by the authors in [2], leverages the insight that audio segments within a single 
cluster are not only similar to one another, but also dis-play the same patterns 
of differences with audio segments be-longing to all other clusters. 
First, speaker clustering perform-ance using the standard Bayesian Information Criterion (BIC) 
is compared to the performance achieved using a BIC-based algo-rithm incorporating global similarity. 
Then, both clustering techniques are tested using an extended feature set including 
F0-derived features in addition to the standard cepstral features. 
The evaluation, which is performed on data recorded from German language radio, 
shows that in most cases F0-features outperform the cepstral-only feature set both 
in standard BIC clustering and in the BIC global-similarity-based approach.