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Research Article Open Access
Speech recognition is a natural means of interaction for a human with a smart assistive environment. In order for this interaction to be effective, such a system should attain a high recognition rate even under adverse conditions. In Speech Recognition speech signals are automatically converted into the corresponding sequence of words in text. When the training and testing conditions are not similar, statistical speech recognition algorithms suffer from severe degradation in recognition accuracy. So we depend on intelligent and recognizable sounds for common communications. In this paper, we first give a brief overview of Speech Recognition and then describe some feature extraction and classifier technique. We have compared MFCC, LPC and PLP feature extraction techniques. We efficiently tested the performance of MFCC is more efficient and accurate then LPC and PLP feature extraction technique in voice recognition and thus more suitable for practical applications.
Feature extraction, feature matching, MFCC, LPC, PLP, ANN, HMM, DTW, Vector Quantization, Gaussian Mixture Model., Extraction Chromatography