Isolated Command Recognition Using MFCC and Clustering Algorithm

Abstract

People who cannot walk by themselves need someone to carry them around on a wheelchair. A voice recognition system was developed to recognize a set of commands used by people with disability to control their wheelchair and devices around them. Voice samples of various commands from 8 different speakers were collected. The features of the collected samples were extracted using MFCC feature extraction technique. The MFCC (Mel-frequency cepstral coefficient) feature extraction technique involves pre-emphasis, framing, windowing, FFT, Mel-scale transformation operations. The major reason for choosing MFCC extraction is that the human ear has a response of a logarithmic scale and not a linear scale. Hence, all the framed voice samples are transformed to the Mel-scale with a logarithmic response and stored in the form of K-means clusters. Features extracted from test data are applied to the models developed for commands, and based on the minimum distance criterion, the model is selected to be closely associated with the respective voice command. The voice recognition system was developed for an 8-command model using MATLAB.