Abstract
In text-independent speaker recognition, Gaussian Mixture Models (GMMs) are widely employed as statistical models of the speakers. It is assumed that the Expectation Maximization (EM) algorithm can estimate the optimal model parameters such as weight, mean and variance of each Gaussian model for each speaker. However, this is not entirely true since there are practical limitations, such as limited size of the training database and uncertainties in the model parameters. As is well known in the literature, limited-size databases is one of the largest challenges in speaker recognition research. In this paper, we investigate methods to overcome the database and parameter uncertainty problem. By reformulating the GMM estimation problem in a Bayesian-optimal way (as opposed to ML-optimal, as with the EM algorithm), we are able to change the GMM parameters to better cope with limited database size and other parameter uncertainties. Experimental results show the effectiveness of the proposed approach.
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Garcia, G., Jung, SK., Eriksson, T. (2007). Bayes-Optimal Estimation of GMM Parameters for Speaker Recognition. In: Müller, C. (eds) Speaker Classification II. Lecture Notes in Computer Science(), vol 4441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74122-0_13
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DOI: https://doi.org/10.1007/978-3-540-74122-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74121-3
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