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

AER

definition

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

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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

蚊帐

Adenoma

弄脏

Vo2-Max

弓箭

Generator

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

比目鱼

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.