Proposing a Robust Classifier for Speech Recognition Based on Synergy Clustering and Observations Frequency

Speech recognition as one of the important branches of speech processing has been attractive for researchers and scientist, from long time ago. Speech recognition is a kind of technology able to determine the pronounced word (s) shown by acoustic signal. The complexity of speech recognition systems depends on the extracted features, their dimensions and the applied classifier. In this paper, we propose a new classifier which is able to compute two matrices “winner” and “minimum distance” in a knowledge extraction phase, as a suitable model for any reference word using synergy clustering and frequency of observations. In the recognition phase, the proposed method is able to determine the similarity between inputted unknown speech and word reference models based on a penalty-reward mechanism. In order to evaluate the proposed method, the FARSDAT data set is used. The results of several experiments on clean and noisy signals show more resistant against noise, higher accuracy and less time complexity for the proposed method, in comparison to the HMM-based speech recognition system.