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A weighted Viterbi algorithm (HMM) is proposed and applied in combination with spectral subtraction and Cepstral Mean Normalization to cancel both additive and convolutional noises in speech recognition. The weighted Viterbi approach is compared and used in combination with state duration modelling. The results presented in this paper show that a proper weight on the information provided by static parameters can substantially reduce the error rate, and that the weighting procedure improvesdoi:10.1109/icassp.1998.675363 dblp:conf/icassp/YomaMJ98 fatcat:oekauuj76fcwnh3kekn227743q