Abstract
This paper compares performances between Gaussian Mixture Model (GMM) classifier and polynomial classifier for text-independent speaker identification. The MFCC feature set has been used for this comparison. Experimental evaluation was conducted on the POLYCOST database with 130 speakers. The importance of the prior in the polynomial classifier has been discussed in detail. Results reveal that, the identification accuracy of the polynomial classifier strongly depends on the choice of prior. For proper prior selection the polynomial classifier can perform better than the GMM classifier.
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Sen, N., Basu, T.K. (2012). A Critical Comparison between GMM Classifier and Polynomial Classifier for Text-Independent Speaker Identification. In: Sambath, S., Zhu, E. (eds) Frontiers in Computer Education. Advances in Intelligent and Soft Computing, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27552-4_74
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DOI: https://doi.org/10.1007/978-3-642-27552-4_74
Publisher Name: Springer, Berlin, Heidelberg
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