Wavelet LPC With Neural Network for Speaker Identification System

摘要

In this study, an average framing linear prediction coding (AFLPC) technique for text-independent speaker identification systems is proposed. The study of the combination of modified LPC with wavelet transform (WT), termed AFLPC, is presented for speaker identification based on our previous paper. The study procedure is based on feature extraction and voice classification. In the phase of classification, feed forward back propagation neural network (FFBPN) is applied because of its rapid response and ease in implementation. In the practical investigation, performance of different wavelet transforms in conjunction with AFLPC were compared with one another. In addition, the capability analysis on the proposed system was examined by comparing it with other systems proposed in literature. Consequently, the FFBPN classifier achieves a better recognition rate (97.36%) with the wavelet packet (WP) and AFLPC termed WPLPCF feature extraction method. It is also suggested to analyze the proposed system in additive white Gaussian noise (AWGN) and real noise environments.