منابع مشابه
Hidden semi-Markov models
Article history: Received 14 April 2009 Available online 17 November 2009
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A semi-Markov HMM (more properly called a hidden semi-Markov model, or HSMM) is like an HMM except each state can emit a sequence of observations. Let Y (Gt) be the subsequence emitted by “generalized state” Gt. The “generalized state” usually contains both the automaton state, Qt, and the length (duration) of the segment, Lt. We will define Y (Gt) to be the subsequence yt−l+1:t. After emitting...
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Many interesting human actions involve multiple interacting agents and also have typical durations. Further, there is an inherent hierarchical organization of these activities. In order to model these we introduce a new family of hidden Markov models (HMMs) that provide compositional state representations in both space and time and also a recursive hierarchical structure for inference at higher...
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There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM’s strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can exten...
متن کاملHierarchical Dirichlet Process Hidden Semi-Markov Models
Given a set of sequential data in an unsupervised setting, we often aim to infer meaningful states present in the data along with characteristics that describe and distinguish those states. For example, in a speaker diarization (or who-spoke-when) problem, we are given a single audio recording of a meeting and wish to infer the number of speakers present, when they speak, and some characteristi...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2010
ISSN: 0004-3702
DOI: 10.1016/j.artint.2009.11.011