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Conference Papers Year : 2011

Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm

Abstract

Hidden Markov models are widely used for recognition algorithms (speech, writing, gesture, ...). In this paper, a classical set of models is considered: state space of hid- den variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parame- ters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maxi- mization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is com- pared to other methods based on a k-mean algorithm on the data with numerical simulations.
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Dates and versions

hal-00620012 , version 1 (07-09-2011)

Identifiers

  • HAL Id : hal-00620012 , version 1

Cite

Pauline Larue, Pierre Jallon, Bertrand Rivet. Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm. EUSIPCO 2011 - 19th European Signal Processing Conference, Aug 2011, Barcelone, Spain. pp.951-955. ⟨hal-00620012⟩
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