標題: Titlebook: Automatic Speech Recognition; A Deep Learning Appr Dong Yu,Li Deng Book 2015 Springer-Verlag London 2015 Adaptive Training.Automatic Speech [打印本頁] 作者: advocate 時間: 2025-3-21 16:11
書目名稱Automatic Speech Recognition影響因子(影響力)
書目名稱Automatic Speech Recognition影響因子(影響力)學科排名
書目名稱Automatic Speech Recognition網(wǎng)絡公開度
書目名稱Automatic Speech Recognition網(wǎng)絡公開度學科排名
書目名稱Automatic Speech Recognition被引頻次
書目名稱Automatic Speech Recognition被引頻次學科排名
書目名稱Automatic Speech Recognition年度引用
書目名稱Automatic Speech Recognition年度引用學科排名
書目名稱Automatic Speech Recognition讀者反饋
書目名稱Automatic Speech Recognition讀者反饋學科排名
作者: 希望 時間: 2025-3-21 23:24 作者: CRATE 時間: 2025-3-22 03:17 作者: 胡言亂語 時間: 2025-3-22 06:04 作者: KIN 時間: 2025-3-22 11:04 作者: 輕浮思想 時間: 2025-3-22 13:38
Psychology from an Islamic Perspectivehe RNN, which exploits the structure called long-short-term memory (LSTM), and analyzes its strengths over the basic RNN both in terms of model construction and of practical applications including some latest speech recognition results. Finally, we analyze the RNN as a bottom-up, discriminative, dyn作者: 合并 時間: 2025-3-22 19:59 作者: SPASM 時間: 2025-3-22 21:39 作者: expository 時間: 2025-3-23 02:42
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 tr作者: Angioplasty 時間: 2025-3-23 08:58 作者: AER 時間: 2025-3-23 12:33 作者: definition 時間: 2025-3-23 17:19
Eiji Arai,Keiichi Shirase,Hidefumi Wakamatsu and slows down both the training and decoding. In this chapter, we discuss algorithms and engineering techniques that speedup the training and decoding. Specifically, we describe the parallel training algorithms such as pipelined backpropagation algorithm, asynchronous stochastic gradient descend a作者: 黃油沒有 時間: 2025-3-23 21:20
Financial Crisis, Trade, and Fragmentation,on problem. In this chapter, we introduce the sequence-discriminative training techniques that match better to the problem. We describe the popular maximum mutual information (MMI), boosted MMI (BMMI), minimum phone error (MPE), and minimum Bayes risk (MBR) training criteria, and discuss the practic作者: 蚊帳 時間: 2025-3-24 00:03 作者: Adenoma 時間: 2025-3-24 05:15 作者: 弄臟 時間: 2025-3-24 07:37 作者: Vo2-Max 時間: 2025-3-24 13:15 作者: 弓箭 時間: 2025-3-24 17:05 作者: Generator 時間: 2025-3-24 19:01
Lewis R. Gordon,LaRose T. Parristional network (CN), a unified framework for describing arbitrary learning machines, such as deep neural networks (DNNs), computational neural networks (CNNs), recurrent neural networks (RNNs), long short term memory (LSTM), logistic regression, and matrixum entropy model, that can be illustrated as作者: 比目魚 時間: 2025-3-25 00:59
I. Alexandrescu,H.-J. Franke,T. VietorAutomatic speech recognition (ASR) is an important technology to enable and improve the human–human and human–computer interactions. In this chapter, we introduce the main application areas of ASR systems, describe their basic architecture, and then introduce the organization of the book.作者: Substitution 時間: 2025-3-25 06:31 作者: 碎石頭 時間: 2025-3-25 09:11
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.作者: Overthrow 時間: 2025-3-25 13:15 作者: CANDY 時間: 2025-3-25 17:38 作者: 和平 時間: 2025-3-25 22:47 作者: 暗指 時間: 2025-3-26 03:51
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.作者: narcissism 時間: 2025-3-26 06:06
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.作者: Disk199 時間: 2025-3-26 12:32 作者: Occlusion 時間: 2025-3-26 15:20
Signals and Communication Technologyhttp://image.papertrans.cn/b/image/166448.jpg作者: Forehead-Lift 時間: 2025-3-26 17:28
https://doi.org/10.1007/978-1-4471-5779-3Adaptive Training; Automatic Speech Recognition; Computational Network; Deep Generative Model; Deep Lear作者: heartburn 時間: 2025-3-26 21:38 作者: amygdala 時間: 2025-3-27 03:55 作者: Accommodation 時間: 2025-3-27 07:41 作者: V切開 時間: 2025-3-27 11:20 作者: 消極詞匯 時間: 2025-3-27 15:11 作者: 有惡意 時間: 2025-3-27 19:21
Kai Mertins,Oliver Krause,Burkhard Schallockontinuous speech recognition tasks. We describe the architecture and the training procedure of the DNN-HMM hybrid system and point out the key components of such systems by comparing a range of system setups.作者: Ornithologist 時間: 2025-3-28 00:45 作者: 謙卑 時間: 2025-3-28 02:51 作者: 恃強凌弱的人 時間: 2025-3-28 09:38 作者: Eviction 時間: 2025-3-28 13:49
Tobias Liebeck,Tobias Meyer,Eberhard AbeleDNNs on the following topics: the restricted Boltzmann machine (RBM), which by itself is an interesting generative model, the deep belief network (DBN), the denoising autoencoder, and the discriminative pretraining.作者: perjury 時間: 2025-3-28 15:08 作者: 群島 時間: 2025-3-28 21:07 作者: Neonatal 時間: 2025-3-29 01:23 作者: configuration 時間: 2025-3-29 03:31
Deep Neural Network Sequence-Discriminative Trainingximum mutual information (MMI), boosted MMI (BMMI), minimum phone error (MPE), and minimum Bayes risk (MBR) training criteria, and discuss the practical techniques, including lattice generation, lattice compensation, frame dropping, frame smoothing, and learning rate adjustment, to make DNN sequence-discriminative training effective.作者: TAG 時間: 2025-3-29 10:14
Representation Sharing and Transfer in Deep Neural Networksared and transferred across related tasks through techniques such as multitask and transfer learning. We will use multilingual and crosslingual speech recognition as the main example, which uses a shared-hidden-layer DNN architecture, to demonstrate these techniques.作者: objection 時間: 2025-3-29 12:32
-Pseudo-Differential Operators,raining, and subspace methods. We further show that adaptation in DNNs can bring significant error rate reduction at least for some speech recognition tasks and thus is as important as that in the GMM systems.作者: maudtin 時間: 2025-3-29 15:44
Lewis R. Gordon,LaRose T. Parrisesents a matrix operation upon its children. We describe algorithms to carry out forward computation and gradient calculation in CN and introduce most popular computation node types used in a typical CN.作者: incision 時間: 2025-3-29 20:57
Adaptation of Deep Neural Networksraining, and subspace methods. We further show that adaptation in DNNs can bring significant error rate reduction at least for some speech recognition tasks and thus is as important as that in the GMM systems.作者: 補充 時間: 2025-3-30 03:07 作者: duplicate 時間: 2025-3-30 06:01 作者: 藝術(shù) 時間: 2025-3-30 12:09
Gaussian Mixture Modelsn random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their parameters specified. This introduction leads to the Gaussian mixture model (GMM) when the distribution作者: 不怕任性 時間: 2025-3-30 14:47 作者: 眨眼 時間: 2025-3-30 17:37
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 tr
