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Deep neural network(DNN) has achieved a great success in automatic speech recognition(ASR), and it can be regarded as a joint model combining the nonlinear feature transformation and the log-linear classifier. Recently DNN is adopted as a regression model to enhance the distorted feature in noisy condition and the enhanced feature is utilized to improve the performance of DNN based ASR. Previous work...
In recent years, deep neural network(DNN) has achieved great success when used as acoustic model in speech recognition. An important application of DNN is to derive bottleneck feature. In this paper, firstly we investigate the robustness of bottleneck features generated by three types of DNN structures on the Aurora 4 task without any explicit noise compensation. Secondly, we propose the node-pruning...
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 by regularized or...
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