Arabic Speech Recognition Based on Knn, J48, and LVQ

Abstract

Most systems of speaker recognition work on speech feature primarily classified of being a low level which considerably relies on speaker physical characteristics and, to the lower extent, the acquired speaking habits. In this paper present a system to recognition and identification in Arabic speaker. It includes two phases (training phase and testing phase) each phase includes the using of audio features (Mean, Standard Division, Zero Crossing, Amplitude). after get the feature, the recognition step is using (J48, KNN, LVQ),) where the Nearest Neighbor (KNN) applied o get the similarity of the data training and data testing , LVQ neural network used for Speech Recognition and Arabic language Identification. This sentence contains words especially kidnappings and kidnappers are ten sentences and pronounce these sentences by 10 people, five men and five women of different ages and each of the ten pronunciation of all sentences, so a total of 100 samples and the samples were recorded on audio and wave. The results of the sentences pronounced by women are higher than the results of the same sentences pronounced by men. They achieved better recognition rate 85, 93, 96.4%