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Titlebook: Automatic Speech Recognition; A Deep Learning Appr Dong Yu,Li Deng Book 2015 Springer-Verlag London 2015 Adaptive Training.Automatic Speech

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21#
發(fā)表于 2025-3-25 06:31:21 | 只看該作者
22#
發(fā)表于 2025-3-25 09:11:11 | 只看該作者
Deep Neural Networkseech recognition systems, and are the focus of the rest of the book. We depict the architecture of DNNs, describe the popular activation functions and training criteria, illustrate the famous backpropagation algorithm for learning DNN model parameters, and introduce practical tricks that make the training process robust.
23#
發(fā)表于 2025-3-25 13:15:40 | 只看該作者
24#
發(fā)表于 2025-3-25 17:38:41 | 只看該作者
25#
發(fā)表于 2025-3-25 22:47:44 | 只看該作者
26#
發(fā)表于 2025-3-26 03:51:08 | 只看該作者
M. Pena,E.S. Ibragimova,M.K. Thompsoneech recognition systems, and are the focus of the rest of the book. We depict the architecture of DNNs, describe the popular activation functions and training criteria, illustrate the famous backpropagation algorithm for learning DNN model parameters, and introduce practical tricks that make the training process robust.
27#
發(fā)表于 2025-3-26 06:06:18 | 只看該作者
https://doi.org/10.1007/978-1-349-11724-6ttleneck approach in which DNNs are used as feature extractors. The hidden layers, which are better representation than the raw input feature, are used as features in the GMM systems. We then introduce techniques that fuse the recognition results and frame-level scores of the DNN-HMM hybrid system with that of the GMM-HMM system.
28#
發(fā)表于 2025-3-26 12:32:49 | 只看該作者
29#
發(fā)表于 2025-3-26 15:20:46 | 只看該作者
Signals and Communication Technologyhttp://image.papertrans.cn/b/image/166448.jpg
30#
發(fā)表于 2025-3-26 17:28:49 | 只看該作者
https://doi.org/10.1007/978-1-4471-5779-3Adaptive Training; Automatic Speech Recognition; Computational Network; Deep Generative Model; Deep Lear
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