Automatic Speech Recognition: A Deep Learning Approach by Dong Yu

By Dong Yu

This e-book offers a finished review of the new development within the box of automated speech reputation with a spotlight on deep studying types together with deep neural networks and lots of in their versions. this can be the 1st computerized speech reputation ebook devoted to the deep studying process. as well as the rigorous mathematical remedy of the topic, the e-book additionally offers insights and theoretical origin of a sequence of hugely winning deep studying models.

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Additional resources for Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology)

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We will return to this important problem of characterizing speech features after discussing a model, the HMM, for characterizing temporal properties of speech in the next chapter. References 1. : A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report, TR-97-021, ICSI (1997) 2. : What HMMs can do. IEICE Trans. Inf. Syst. E89-D(3), 869–891 (2006) 3. : Pattern Recognition and Machine Learning. Springer, Heidelberg (2006) 4.

We then provide discussions on the use of the HMM as a generative model for speech feature sequences and its use as the basis for speech recognition. Finally, we discuss the limitations of the HMM, leading to its various extended versions, where each state is made associated with a dynamic system or a hidden time-varying trajectory instead of with a temporally independent stationary distribution such as a Gaussian mixture. These variants of the HMM with state-conditioned dynamic systems expressed in the state-space formulation are introduced as a generative counterpart of the recurrent neural networks to be described in detail in Chap.

The fundamental characterization of a continuous-valued random variable, x, is its distribution or the probability density function (PDF), denoted generally by p(x). The PDF for a continuous random variable at x = a is defined by P(a − Δa < x ≤ a) . 1) where P(·) denotes the probability of the event. The cumulative distribution function of a continuous random variable x evaluated at x = a is defined by . P(a) = P(x ≤ a) = a p(x)d x. 2) −∞ A PDF has to satisfy the property of normalization: ∞ P(x ≤ ∞) = p(x)d x = 1.

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