A MULTI-RESOLUTION HIDDEN MARKOV MODEL USING CLASS-SPECIFIC FEATURES

We address the problem in signal classification applications, such as automatic speech recognition (ASR) systems that employ the hidden Markov model (HMM), that it is neces-sary to settle for a fixed analysis window size and a fixed feature set. This is despite the fact that complex signals such as human speech typically contain a wide range of sig-nal types and durations. We apply the probability density function (PDF) projection theorem to generalize the hidden Markov model (HMM) to utilize a different features and seg-ment length for each state. We demonstrate the algorithm using speech analysis so that long-duration phonemes such as vowels and short-duration phonemes such as plosives can utilize feature extraction tailored to the their own time scale. 1