Abstract
When using probabilistic neural network (PNN) to recognize human speaker, there exists structure complex problems if the training sample amount is large and the redundancy degree is high. To overcome this shortcoming, this paper proposes a method of principal component analysis (PCA) for keeping the effective information and reducing the redundancy of characteristic parameters, that means, this method can reduce the dimension of input data and optimize the structure of PNN network successfully. Experimental results show that the proposed speaker recognition method based on the combination of principal component analysis (PCA) and probabilistic neural network (PNN) is an effective and reliable new speaker recognition system.
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Zhou, Y., Shang, L. (2012). Speaker Recognition Based on Principal Component Analysis and Probabilistic Neural Network. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_93
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DOI: https://doi.org/10.1007/978-3-642-25944-9_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
Online ISBN: 978-3-642-25944-9
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