  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 k-nearest 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.
