Speech recognition using vector quantization

This paper presents a novel method for isolated English word recognition based on energy and zero crossing features with vector quantization. This isolated word recognition method consists of two phases, feature extraction phase and recognition phase. In feature extraction, end points are detected and noise is removed using end point detection algorithm, a feature vector is obtained by combining the energy and zero cross rate into a single vector of twenty dimensions. Recognition phase consists of two steps, feature training and testing, in feature training, codebooks for each reference samples are generated using LBG Vector Quantization algorithm. For testing Euclidean distance is calculated between test sample feature vector and codebook of all reference speech samples. Speech sample with minimum average distance is selected. Experimental results showed that the maximum recognition rate of 85% is obtained for codebook size of 4.