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2010

A multi-resolution hidden Markov model using class-specific features

11 years 8 months ago
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 necessary 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 signal 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 segment 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.
Paul M. Baggenstoss
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSP
Authors Paul M. Baggenstoss
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