By Dong Yu

ISBN-10: 1447157788

ISBN-13: 9781447157786

ISBN-10: 1447157796

ISBN-13: 9781447157793

This ebook presents a accomplished evaluate of the hot development within the box of automated speech acceptance with a spotlight on deep studying versions together with deep neural networks and lots of in their variations. this can be the 1st automated speech popularity e-book devoted to the deep studying strategy. as well as the rigorous mathematical therapy of the topic, the e-book additionally offers insights and theoretical starting place of a chain of hugely winning deep studying models.

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Extra resources for Automatic Speech Recognition: A Deep Learning Approach

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It is well-known that speech is produced by modulating a relatively small number of parameters of a dynamical system [7, 8, 17, 20, 29, 30]. This suggests that the true underlying structure of speech is of a much lower dimension than is immediately apparent in a window that contains hundreds of coefficients. Therefore, other types of model, which can capture better properties of speech features, are expected to work better than GMMs for acoustic modeling of speech. In particular, the new models should more effectively exploit information embedded in a large window of frames of speech features than GMMs.

Even if log p(y; θ ) can usually be easily expressed in closed form, finding the closed-form expression for the expectation is usually hard. 2 Applying EM to Learning the HMM—Baum-Welch Algorithm We now discuss how maximum-likelihood parameter estimation and, in particular, the EM algorithm is applied to solve the learning problem for the HMM. As introduced in the preceding section, the EM algorithm is a general iterative technique for maximum likelihood estimation, with local optimality in general, when hidden variables exist.

T using definitions in Eqs. 18. Note that T |ot , q = i) = P(o T |q = i) because the observations are IID given the P(ot+1 1 t t+1 t state in the HMM. Given this, P(o1T ) can be computed as N N P(o1T ) = P(qt = i, o1T ) = i=1 αt (i)βt (i). 24) i=1 Taking t = T in Eq. 24 and using Eq. 22 lead to N P(o1T ) = αT (i). 25) i=1 Thus, strictly speaking, the β recursion is not necessary for the forward scoring computation, and hence the algorithm is often called the forward algorithm. 32 3 Hidden Markov Models and the Variants However, the β computation is a necessary step for solving the model parameter estimation problem, which will be covered in the next section.

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Automatic Speech Recognition: A Deep Learning Approach by Dong Yu

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