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Model based VAD approaches have been widely used and achieved success in practice. These approaches usually cast VAD as a frame-level classification problem and employ statistical classifiers, such as Gaussian Mixture Model (GMM) or Deep Neural Network (DNN) to assign a speech/silence label for each frame. Due to the frame independent assumption classification, the VAD results tend to be fragile....
A recent trend in normalization of factors extraneous to a speech recognition task has been to explicitly introduce features related to the unwanted variability in the training of Deep Neural Networks (DNN). Typically, this is done by either perturbing the training set with models of these extraneous factors such as vocal tract length and environmental noise or augmenting the conventional spectral...
Although deep neural networks (DNNs) have achieved great success in automatic speech recognition (ASR), significant performance degradation still exists in noisy environments. In this paper, a novel multi-task joint-learning framework is proposed to address the noise robustness for speech recognition. The architecture integrates two different DNNs, including the regressive denoising DNN and the discriminative...
Nowadays, cloud storage systems may contain tens of thousands of servers and large scale data sets, which significantly require efficient data management scheme and query processing mechanism. To fulfill these requirements in modern data centers, the infrastructure of cloud systems, we propose FT-Index, a secondary indexing scheme for cloud system with switch-centric topology. FT-Index has a two-layer...
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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