Isolated Word Recognition System Using Back Propagation Network for Tamil Spoken Language

Abstract

Recently with the wide development of computers, various forms of information exchange between human and computer are discovered. At present, interacting with the computer using speech is one of the active scientific research fields especially for people with disabilities who face variety of barriers to computer use. Such research in Automatic Speech Recognition (ASR) is investigated for different languages because each language has its specific features. This paper presents a speech recognition system for individually spoken word in Tamil language using multilayer feed forward network which falls under the category, "Networks for Classification and Prediction" and has widespread interesting applications and functions related to speech processing. To implement the above system, initially preprocessing is done with the input signal and the speech features being the main part of speech recognition system, are analyzed and extracted via Mel Frequency Cepstral Coefficients (MFCC). These feature vectors are given as the input to the Feed-Forward Neural Network for classifying and recognizing Tamil spoken word. Experiments are done with sample Tamil speech signals and its performance are measured based on Mean square error (MSE). The results indicate that the adopted network with specified parameters have produced the best MSE.