Paper Title
Feed Forward Back Propagation Neural Network For Speaker Independent Speech Recognition

Speaker recognition is to make sure that the person to be claimed is the correct person or not. Speaker recognition can be divided into speaker identification and speaker verification. Speaker identification decides whether the speaker is from the specified group. In speaker verification, a person makes an identity claim. The two main stages in this technique are feature extraction and feature matching. Feature extraction is the process in which we extract some useful parameters, which is used to represent the speaker. Feature matching involves identification of the unknown speaker by comparing the features extracted of the unknown speaker with the enrolled voices of the known speakers. To extract the features of speech signal, Mel frequency Cepstral Coefficients (MFCC) and for feature matching the multi-layer perceptron method in artificial neural network (ANN) has been used. A Feed Forward Back Propagation Neural Network (FFBPNN) is used to classify the voices of various speakers in the learning or training phase. The network is tested with samples from the various speakers. During the learning phase many parameters are tested with, number of selected coefficients, number of hidden nodes and value of momentum parameter. In the testing phase the recognition performance is computed for each value of the above parameters.