Segmental Hidden Markov Models with Random Effects for Waveform Modeling
Seyoung Kim, Padhraic Smyth; 7(33):945−969, 2006.
This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and recognition of new waveforms. Expectation-maximization (EM) algorithms are derived and investigated for fitting such models to data. In particular, the "expectation conditional maximization either" (ECME) algorithm is shown to provide significantly faster convergence than a standard EM procedure. Experimental results on two real-world data sets demonstrate that the proposed approach leads to improved accuracy in classification and segmentation when compared to alternatives such as Euclidean distance matching, dynamic time warping, and segmental HMMs without random effects.
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