Abstract：To overcome the drawbacks of original speech features that long temporal speeches and the supervised information can not be effectively utilized and the training time cost is high, a bottleneck feature extraction method based on hierarchical deep sparse belief network is presented. The overlapping group lasso is used as the sparse regularization constraint of the objective function of deep belief network to obtain a deep sparse belief network with a higher speed. To make full use of the hierarchical structure, two sparse deep belief networks are connected in series to enhance the discriminant ability of the bottleneck features. The experimental results on phoneme recognition task show that the proposed feature is effective.
王一，杨俊安，刘辉，柳林. 基于层次稀疏DBN的瓶颈特征提取方法*[J]. 模式识别与人工智能, 2015, 28(2): 173-180.
WANG Yi, YANG Jun-An, LIU Hui, LIU Lin. Bottleneck Feature Extraction Method Based on Hierarchical Deep Sparse Belief Network. , 2015, 28(2): 173-180.
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2015, Vol. 28 
