Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A high speed unsupervised speaker retrieval using vector quantization
  and second-order statistics   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.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph