Comparison GMM and SVM Classifier for Automatic Speaker Verification
Shamama Afnan · TigerPrints (Clemson University) · 2015
The objective of this thesis is to develop automatic text-independent speaker verification systems using unconstrained telephone conversational speech. We began by performing a Gaussian Mixture Model Likelihood ratio verification task in speaker independent system as described by MIT Lincoln Lab. We next introduced a speaker dependent verification system based on speaker dependent thresholds. We then implemented the same system applying Support Vector Machine. In SVM, we used polynomial kernels and radial basis function kernels and compared the performance. For training and testing the system, we used low-level spectral features. Finally, we provided a performance assessment of these systems using the National Institute of Standards and technology (NIST) speaker recognition evaluation 2008 telephone corpora.