hidden Markov model


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hidden Markov model

[¦hid·ən ′mär·kəf ‚mäd·əl]
(mathematics)
A finite-state machine that is also a doubly stochastic process involving at least two levels of uncertainty: a random process associated with each state, and a Markov chain, which characterizes the probabilistic relationship among the states in terms of how likely one state is to follow another.
References in periodicals archive ?
Including over 7,000 Hidden Markov Models, it provides structural assignments to protein sequences and enables researchers to detect broader and more distant evolutionary relationships through sophisticated remote homology detection.
In this paper, we present a new hybrid approach for isolated spoken word recognition using Hidden Markov Model models (HMM) combined with Dynamic time warping (DTW).
Human Activity Recognition in Archaeological Sites by Hidden Markov Models.
The overall detection rates for the neural network and the Hidden Markov Model were greater than 90%.
A discrete output, first-order Hidden Markov Model (HMM) is a finite state automaton and can be represented by a 5-tuple {S, V, D, A, B}, where S is a set of values for the hidden states, S ={[s.
Included an expert treatment decision path analysis, independently published epidemiology and quality of life utility assumptions and a robust hidden Markov model methodology
Initially, ECRL was formed to further develop Cambridge University's Hidden Markov Model Toolkit (HTK) for speech recognition research and development.
According to Roger Byford, president and CEO of Vocollect, BlueStreak utilizes Hidden Markov Model (HMM) speech recognition technology.
A Hidden Markov Model (11MM) is a stochastic Finite State Machine, a tool that has been used for speech recognition in the past.
Keywords in this release: voice recognition technology, Speech Application Language Tags, SALT, Voice Extensible Markup Language, VoiceXML, Interactive Voice Response, IVR, Hidden Markov Model, HMM, Digital Signal Processing, DSP, Media Processing Server, MPS, OpenSpeech(TM) Recognizer, OSR
A wide range of signal processing tools such as various wavelet transform techniques, de-noising and filtering methods, blind source separation methodologies, ICA, EMD, and hidden Markov model have been or are being applied in the development of the system.
Finally statistical results from the experiments showed that using hidden markov model is the best approach for detecting abnormal behaviour in programs for both Active and inactive analysis for protecting our files and system processes from attack by malicious programs.