Feature Extraction Methods LPC, PLP and MFCC (original) (raw)

The automatic recognition of speech, enabling a natural and easy to use method of communication between human and machine, is an active area of research. Speech processing has vast application in voice dialing, telephone communication, call routing, domestic appliances control, Speech to text conversion, text to speech conversion, lip synchronization, automation systems etc. Nowadays, Speech processing has been evolved as novel approach of security. Feature vectors of authorized users are stored in database. Speech features are extracted from recorded speech of a male or female speaker and compared with templates available in database. Speech can be parameterized by Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Mel Frequency Ce pstral Coefficients (MFCC) PLP-RASTA (PLP-Relative Spectra) etc. Some parameters like PLP and MFCC considers the nature of speech while it extracts the features, while LPC predicts the future features based on previous features. Training models like neural network are trained for feature vector to predict the unknown sample. Techniques like Vector Quantization (VQ), Dynamic Time Warping (DTW), Support Vector Machine (SVM), and Hidden Markov Model (HMM) can be used for classification and recognition. We have described neural network in our paper with LPC, PLP and MFCC parameters.