The aim of this work is to enhance the performance of speaker identification using Hopfield neural network algorithm. Speech signals are collected from VALID Audio-Visual dataset and some speech signal pre-processing techniques are applied to process the speech to feed the Hopfield neural network algorithm. Filtering technique is applied to remove the noises from the speech signals. MFCC based standard speech feature extraction technique is used to extract the speech features. These speech features are used for the learning and recognition model of Hopfield neural network. VALID audio-visual database has been used to measure the performance of the proposed system. Keywords: Speaker identification, speech start and end points detection, speech feature extraction, Hopfield neural networkCite this Article Md. Rabiul Islam, Md. Fayzur Rahman. Speaker Identification using Hopfield Neural Network based Classifier. Journal of Network Security (JoNS). 2015; 3(2): 32–35p.
