Abstract
To improve the performance of speaker recognition, the embedded linear transformation is used to integrate both transformation and diagonal-covariance Caussian mixture into a unified framework. In the case, the mixture number of GMM must be fixed in model training. The cluster expectation-maximization (EM) algorithm is a well-known technique in which the mixture number is regarded as an estimated parameter. This paper presents a new model structure that integrates a multi-step cluster algorithm into the estimating process of GMM with the embedded transformation. In the approach, the transformation matrix, the mixture number and model parameters are simultaneously estimated according to a maximum likelihood criterion. The proposed method is demonstrated on a database of three data sessions for text independent speaker identification. The experiments show that this method outperforms the traditional GMM with cluster EM algorithm.
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Xu, L., Tang, Z. Speaker identification using multi-step clustering algorithm with transformation-based GMM. Aut. Conrol Comp. Sci. 41, 224–231 (2007). https://doi.org/10.3103/S0146411607040062
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DOI: https://doi.org/10.3103/S0146411607040062