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This paper proposed an unsupervised learning method to learn speech features based on Dynamic Bayesian Networks (DBNs) that accounts for the spatiotemporal dependences in speech signal. Although deep networks have been successfully applied to unsupervised learning features, the structures of the deep networks are often fixed before learning and they fail to capture temporal representation. In this...
Dynamic Bayesian Networks (DBN) area subset of the probabilistic graphical models (PGM) which include hidden Markov model (HMM) as a special case. One of the principle weaknesses of HMMs is the independence assumptions on the observed and hidden processes of speech. This paper proposed to use the DBN for Tibetan language continuous speech recognition.The proposed approach is based on structure learning...
The research on Tibetan speech recognition is in its initial stage. It is significant to research on recognition algorithm adapted for Tibetan speech. A kind of algorithm of Tibetan speech recognition, based on dynamic Bayesian network (DBN), would be investigated in this paper. The simulation on the given algorithm would be carried out, and through the comparing with the recognizing algorithm based...
MBBNTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBN) and decision tree, would behave better performance than other Bayesian networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically...
MBBCTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBC) and Decision Tree, performances better than other Bayesian Networks for classification. But MBBCTree classifier was built by the traditional passive learning. The available training samples with actual classes are not enough for passive learning method for modelling MBBCTree classifier in practice. Active...
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