By Dong Yu
This booklet offers a finished evaluate of the new development within the box of computerized speech popularity with a spotlight on deep studying types together with deep neural networks and plenty of in their versions. this can be the 1st computerized speech attractiveness e-book devoted to the deep studying method. as well as the rigorous mathematical remedy of the topic, the ebook additionally offers insights and theoretical starting place of a sequence of hugely profitable deep studying models.
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Additional info for Automatic speech recognition. A deep learning approach
This introduction leads to the Gaussian mixture model (GMM) when the distribution of mixture-of-Gaussian random variables is used to fit the real-world data such as speech features. The GMM as a statistical model for Fourier-spectrum-based speech features plays an important role in acoustic modeling of conventional speech recognition systems. We discuss some key advantages of GMMs in acoustic modeling, among which is the easy way of using them to fit the data of a wide range of speech features using the EM algorithm.
37 are taken over states i or over state pairs i, j, which is significantly simpler than the summations over state sequences q1T as in the unsimplified forms of Q 1 (θ|θ0 ) and Q 2 (θ|θ0 ) in Eq. 33. 37 are the simplistic form of the auxiliary objective function, which can be maximized in the M-step discussed next. 2 M-Step The reestimation formulas for the transition probabilities of the Markov chain in the Gaussian HMM can be easily established by setting ∂∂aQi 2j = 0, for Q 2 in Eq. 37 and for i, j = 1, 2, .
At the start, this membership is unknown, or missing. The task of parameter estimation is to learn appropriate parameters for the distribution, with the connection to the data points being represented as their membership in the individual Gaussian distributions. Here, we focus on maximum likelihood methods for parameter estimation of the Gaussian-mixture distribution, and the expectation maximization (EM) algorithm in particular. The EM algorithm is the most popular technique used to estimate the parameters of a mixture given a fixed number of mixture components, and it can be used to compute the parameters of any parametric mixture distribution.