Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Fundamentals of speech recognition
Fundamentals of speech recognition
Learning in graphical models
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Dynamic bayesian networks: representation, inference and learning
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Modeling waveform shapes with random effects segmental hidden Markov models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Modelling ECG signals with hidden Markov models
Artificial Intelligence in Medicine
Classification of seismic signals by integrating ensembles ofneural networks
IEEE Transactions on Signal Processing
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International Journal of Computer Vision
An EM algorithm to learn sequences in the wavelet domain
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Knowledge-Based Systems
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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.