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Subspace methods are used for deep neural network (DNN)-based acoustic model adaptation. These methods first construct a subspace and then perform the speaker adaptation as a point in the subspace. This paper aims to investigate the effectiveness of subspace methods for robust unsupervised adaptation. For the analysis, we compare two state-of-the-art subspace methods, namely, the singular value decomposition...
In this paper, a new learning method tolerant to imprecision is introduced to fuzzy tree (FT) modeling method. The learning method is called ε-insensitive learning or ε learning, where, in order to fit the FT model to real data, the ε-insensitive loss function is used. FT method adaptively partitions the input space and is irrelevant to the dimension of the input space. For the consequent parameters,...
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