Dirichlet mixture models of neural net posteriors for hmm based speech recognition

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In this paper, the authors propose to using Dirichlet Mixture models instead of Gaussian Mixture models for the hybrid NN/HMM system. In the conventional NN/HMM system, the NN's posteriors are Gaussianized to feed into the HMM framework. However, as the posterior probabilities are lying on probability simplex and their distribution could be modeled by Dirichlet distributions. Thus Dirichlet Mixture models would be more preferable to Gaussian Mixture model. 

However, the final system performance, although better than the GMM based system, are still far for the state-of-the-art performance. 

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