Abstract
In this paper, a new model-based clustering algorithm is introduced for optimal speaker modeling in speaker identification systems. The introduced algorithm can estimates the optimal number of mixture components using a cross-validation methodology, as well as, overcome the initialization sensitivity and local maxima problems of classical EM algorithm using a split & merge incremental learning approach. The performed experiments in speaker identification task demonstrate the efficiency and effectivity of the proposed algorithm compared to the commonly used Expectation-Maximization (EM) algorithm.
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Bouziane, A., Kharroubi, J., Zarghili, A. (2017). An Improved Speaker Identification System Using Automatic Split-Merge Incremental Learning (A-SMILE) of Gaussian Mixture Models. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_36
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DOI: https://doi.org/10.1007/978-3-319-57261-1_36
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