Beiqian Dai

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In this paper, we propose a novel image representation for scene classification. Firstly, we model multiple order statistics of image patches via Gaussian Mixture Model(GMM) in a Bayesian framework. Secondly, we combine the information of mean and covariance of the GMM and represent it as a mean-covariance supervector through a new distance metric.(More)
A novel type of Gaussian mixture model for text-independent speaker verification, Hierarchical Gaussian Mixture Model (HGMM) is proposed in this paper. HGMM aims at maximizing the efficiency of MAP training on the Universal Background Model (UBM). Based on the hierarchical structure, the parameters of one Gaussian component can also be adapted by the(More)
A novel discriminative training method of Gaussian mixture model for text-independent speaker verification, Figure of Merit (FOM) training, is proposed in this paper. FOM training aims at maximizing the FOM of a ROC curve by adjusting the model parameters, rather than only approximating the underlying distribution of acoustic observations of each speaker(More)
In this paper we propose to merge speech and handwriting recognition hypotheses together for improving the performance of Chinese character input. The recognition result of handwriting character input can be reliable when the character is written rather squarely. However, more legible of square handwriting tends to slow down the input (stroke writing)(More)