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

Abstract

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.