Error Minimization in Phoneme based Automated Speech Recognition for Similar Sounding Phonemes

The technology of ASR (Automated Speech Recognition) has been quite successful with the use of Hidden Markov Model (HHM) with the aid of probabilistic and the best path methods. The words with limited vocabulary content can easily be modeled and trained. The modules with a large dictionary has to be modeled with context independent phones joining together to form a word. The major problem lies in recognizing the word with similar sounding phonemes. This paper aims in minimizing the error with similar sounding phonemes by using the viterbi algorithm for each similar syllable in the backward direction.