Automatic speech recognition. A deep learning approach by Dong Yu

By Dong Yu

This publication offers a complete evaluate of the new development within the box of automated speech attractiveness with a spotlight on deep studying types together with deep neural networks and lots of in their versions. this is often the 1st automated speech acceptance publication devoted to the deep studying procedure. as well as the rigorous mathematical therapy of the topic, the ebook additionally offers insights and theoretical origin of a chain of hugely profitable deep studying models.

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12) where the posterior probabilities (also called the membership responsibilities) computed from the E-step are given by 1 Detailed derivation of these formulae can be found in [1], which we omit here. Related derivations for similar but more general models can be found in [2, 3, 6, 15, 18]. 18 2 Gaussian Mixture Models ( j) h m (t) = ( j) ( j) ( j) cm N (x(t) ; μm , Σ m ) ( j) n i=1 ci N ( j) ( j) (x(t) ; μi , Σ i ). 13) That is, on the basis of the current (denoted by superscript j above) estimate for the parameters, the conditional probability for a given observation x(t) being generated from mixture component m is determined for each data sample point at t = 1, .

28(4), 357–366 (1980) 5. : Speech Processing—A Dynamic and Optimization-Oriented Approach. Marcel Dekker Inc, New York (2003) 6. : Deep Learning: Methods and Applications. NOW Publishers, Delft (2014) 7. : Perceptual linear predictive (PLP) analysis of speech. J. Acoust. Soc. Am. 87, 1738 (1990) 8. : A practical guide to training restricted Boltzmann machines. Technical Report UTML TR 2010-003, University of Toronto (2010) 9. : Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups.

In these applications, the HMM is used as a powerful model to characterize the temporally nonstationary, spatially variable, but regular, learnable patterns of the speech signal. One key aspect of the HMM as the acoustic model of speech is its sequentially arranged Markov states, which permit the use of piecewise stationarity for approximating the globally nonstationary properties of speech feature sequences. Very efficient algorithms have been developed to optimize the boundaries of the local quasi-stationary temporal regimes, which we will discuss in Sect.

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