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Robust Speaker Identification Using Fusion of Features and Classifiers
2017
International Journal of Machine Learning and Computing
Speaker identification using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, proposed by Reynolds (1995), is one of the most effective approaches available in the literature. The use of GMMs for modeling speaker identity is motivated by the interpretation that the Gaussian components represent some general speaker-dependent spectral shapes, and the capability of mixtures to model arbitrary densities. In this work, we have established empirically
doi:10.18178/ijmlc.2017.7.5.635
fatcat:b5pifjnwbbhbhlia6nc45x2kei