Isolated Word Recognition Using Neural Network

Isolated Word Recognition is the process of converting the spoken word into its corresponding text format. At present mainly Mel Frequency Cepstrum Coefficients (MFCC) is used as the feature extraction parameter i.e. the identifying features for the speech signal. Through this paper efforts have been made to determine the accuracy of an MFCC based system and also to build an isolated word recognizer based on word acoustic model that uses MFCC in combination with other features of speech such as Root Mean Square Energy, Length of the word and its Brightness. Using an artificial neural network as the classifier, the system was trained & tested for a set of spoken isolated words. The results obtained showed a high and an increased accuracy for the experiment in which along with MFCC other selected parameters were also involved against the experiment which only involved MFCC.