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http://dx.doi.org/10.5391/IJFIS.2014.14.4.240

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation  

Jiang, Shuangshuang (Multimedia Research Lab, CECS Dept., University of Louisville)
Frigui, Hichem (Multimedia Research Lab, CECS Dept., University of Louisville)
Calhoun, Aaron W. (Pediatrics Dept., University of Louisville)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.14, no.4, 2014 , pp. 240-248 More about this Journal
Abstract
We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.
Keywords
Speaker identification; Clustering; Bag-of-Words (BoW) feature representation; Fuzzy membership; Possibilistic membership; Naive Bayes classifier;
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