A Critical Comparison between GMM Classifier and Polynomial Classifier for Text-Independent Speaker Identification
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.
KeywordsSpeaker identification GMM classifier Polynomial classifier
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- 4.Campbell, W.M., Torkkola, K., Balakrishnan, S.V.: Dimension reduction techniques for training polynomial networks. In: Proceedings of 17 International Conference of Machine Learning, vol. 200, pp. 1015–1022Google Scholar
- 5.Petrovska, D., et al.: POLYCOST: A Telephonic speech database for speaker recognition. In: RLA2C, Avignon, France, April 20-23, pp. 211–214 (1998)Google Scholar