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Cluster Adaptive Training for Deep Neural Networks

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Although context-dependent DNN -HMM systems have achieved significant improvements over GMM -HMM systems, there still exists big performance degradation if the acoustic condition of the test data mismatches that of the training data. Hence, adaptation and adaptive training of DNN are of great research interest. Previous works mainly focus on adapting the parameters of a single DNN . These methods all require relatively large number of parameters to be estimated during adaptation. In contrast, this paper employs the cluster adaptive training (CAT) framework for DNN adaptation. Here, multiple DNNs are constructed to form the bases of a canonical parametric space. During adaptation, an interpolation vector, specific to a particular acoustic condition, is used to combine the multiple DNN bases into a single adapted DNN .

Biography

Tian Tan is a Ph.D student with Kai Yu at Shanghai Jiao Tong University (China). He is visiting the University of Cambridge till 5th March.

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This talk is part of the CUED Speech Group Seminars series.

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