Natural Language Modeling for Phoneme-to-Text Transcription
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified theory of composite pattern analysis for automatic speech recognition
Computer speech processing
Formal languages
Speech recognition experiment with 10,000 words dictionary
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
An efficient algorithm for the inference of circuit-free automata
Syntactic and structural pattern recognition
Acoustic-phonetic decoding of speech
Proceedings of the NATO Advanced Study Institute on Recent advances in speech understanding and dialog systems
Mathematical foundations of hidden Markov models
Proceedings of the NATO Advanced Study Institute on Recent advances in speech understanding and dialog systems
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Defense of the ansatz for dynamical hierarchies
Artificial Life
Hidden Markov Models with Patterns and Their Application to Integrated Circuit Testing
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Probabilistic Finite-State Machines-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bibliographical study of grammatical inference
Pattern Recognition
Residual languages and probabilistic automata
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Clustering gene expression series with prior knowledge
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
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In the literature on automatic speech recognition, the popular hidden Markov models (HMMs), left-to-right hidden Markov models (LRHMMs), Markov source models (MSMs), and stochastic regular grammars (SRGs) are often proposed as equivalent models. However, no formal relations seem to have been established among these models to date. A study of these relations within the framework of formal language theory is presented. The main conclusion is that not all of these models are equivalent, except certain types of hidden Markov models with observation probability distribution in the transitions, and stochastic regular grammar.