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Although context-dependent DNN-HMM systems have achieved significant improvements over GMM-HMM systems, significant performance degradation has been observed 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 DNN adaptation works mainly focus on adapting parameters of a single DNN...
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...
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...
Deep Neural Network (DNN), which can model hierarchical and complex relationship between input and output layer has recently been applied in speech synthesis. However, it is remained uncertain why DNN outperform traditional HMM-based synthesis. This paper describes several implementation details of DNN-based speech synthesis system and compares different impacting factors, e.g, F0 modeling method...
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