Enhancement in speaker recognition for optimized speech features using GMM, SVM and 1-D CNN

Contemporary automatic speaker recognition (ASR) systems do not provide 100% accuracy making it imperative to explore different techniques to improve it. Easy access to mobile devices and advances in sensor technology, has made voice a preferred parameter for biometrics. Here, a comparative analysis of accuracies obtained in ASR with employment of classical Gaussian mixture model (GMM), support vector machine (SVM) which is the machine learning algorithm and the state of art 1-D CNN as classifiers is presented. Authors propose considering dynamic voice features along with static features as relevant speaker information in them lead to substantial improvement in the accuracy for ASR. As concatenation of features leads to the redundancy and increased computation complexity, Fisher score algorithm was employed to select the best contributing features resulting in improvement in accuracy. The results indicate that SVM and 1-D Neural network outperform GMM. Support Vector Machine (SVM), and 1-D CNN gave comparable results with 1-D CNN giving an improved accuracy of 94.77% in ASR.