Filtering on hidden Markov models

Abs this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes it sufficient to adapt the parameters to the given FIR filter coefficients. Performance of the proposed technique is evaluated and compared to that of the retrained HMM parameters based on the continuous digit recognition experiments.