Wavelet Feature Selection Using Fuzzy Approach to Text Independent Speaker Recognition
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Magnified gradient function with deterministic weight modification in adaptive learning
IEEE Transactions on Neural Networks
Ubiquitous and Robust Text-Independent Speaker Recognition for Home Automation Digital Life
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Expert Systems with Applications: An International Journal
Improved wavelet feature extraction using kernel analysis for text independent speaker recognition
Digital Signal Processing
Clustered-Hybrid Multilayer Perceptron network for pattern recognition application
Applied Soft Computing
Wavelet entropy and neural network for text-independent speaker identification
Engineering Applications of Artificial Intelligence
Removal and interpolation of missing values using wavelet neural network for heterogeneous data sets
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Rule extraction from DEWNN to solve classification and regression problems
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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A wavelet packet feature selection derived by using multilayered neural network for speaker identification is described. The concept of a multilayered neural network is without using a gradient method. First, the outputs of each hidden unit are algebraically determined by an error backpropagation method. Then, the weight parameters are determined by using an exponentially weighted least squares method. Our results have shown that this feature selection introduced better performance than the other methods with respect to the percentages of recognition.