This paper describes an effective unsupervised method for
query-by-example speaker retrieval. We suppose that only
one speaker is in each audio file or in audio segment. The
audio data are modeled using a common universal
codebook. The codebook is based on bag-of-frames (BOF).
The features corresponding to the audio frames are extracted
from all audio files. These features are grouped into clusters
using the K-means algorithm. The individual audio files are
modeled by the normalized distribution of the numbers of
cluster bins corresponding to this file. In the first level the knearest
to the query files are retrieved using vector space
representation. In the second level the second-order
statistical measure is applied to obtained k-nearest files to
find the final result of the retrieval. The described method is
evaluated on the subset of Ester corpus of French broadcast
news.