Substitution
发表于 2025-3-25 06:31:21
http://reply.papertrans.cn/17/1665/166448/166448_21.png
碎石头
发表于 2025-3-25 09:11:11
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.
Overthrow
发表于 2025-3-25 13:15:40
http://reply.papertrans.cn/17/1665/166448/166448_23.png
CANDY
发表于 2025-3-25 17:38:41
http://reply.papertrans.cn/17/1665/166448/166448_24.png
和平
发表于 2025-3-25 22:47:44
http://reply.papertrans.cn/17/1665/166448/166448_25.png
暗指
发表于 2025-3-26 03:51:08
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.
narcissism
发表于 2025-3-26 06:06:18
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.
Disk199
发表于 2025-3-26 12:32:49
http://reply.papertrans.cn/17/1665/166448/166448_28.png
Occlusion
发表于 2025-3-26 15:20:46
Signals and Communication Technologyhttp://image.papertrans.cn/b/image/166448.jpg
Forehead-Lift
发表于 2025-3-26 17:28:49
https://doi.org/10.1007/978-1-4471-5779-3Adaptive Training; Automatic Speech Recognition; Computational Network; Deep Generative Model; Deep Lear