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

A high speed unsupervised speaker retrieval using vector quantization and second-order statistics