Connected word recognition in extreme noisy environment using Weighted State Probabilities (WSP)

Recognition of continuous speech in extreme noisy environments is a difficult task. A novel algorithm is suggested to enhance the performance of recognition in very low SNRs. The left to right HMM Weighted State Probabilities (WSP) method considers not only the probability of getting the given observation sequence, but also the pattern of states probabilities. On a ten digits (Hebrew) recognition task, with SNR of 10 db, the WSP has improved recognition results from 0% to 50%. It is suggested to apply the method, in conjunction with PMC enhancement algorithm, to very low SNR word spotting systems.