Fundamentals of speech recognition
Fundamentals of speech recognition
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs
Data Mining and Knowledge Discovery
Efficient Hidden Semi-Markov Model Inference for Structured Video Sequences
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Incremental construction of structured hidden Markov models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
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Structured Hidden Markov Model (S-HMM) is a variant of Hierarchical Hidden Markov Model that shows interesting capabilities of extracting knowledge from symbolic sequences. In fact, the S-HMM structure provides an abstraction mechanism allowing a high level symbolic description of the knowledge embedded in S-HMM to be easily obtained. The paper provides a theoretical analysis of the complexity of the matching and training algorithms on S-HMMs. More specifically, it is shown that Baum-Welch algorithm benefits from the so called locality property, which allows specific components to be modified and retrained, without doing so for the full model. The problem of modeling duration and of extracting (embedding) readable knowledge from (into) a S-HMM is also discussed.