By Dong Yu
This e-book presents a complete evaluate of the hot development within the box of computerized speech attractiveness with a spotlight on deep studying types together with deep neural networks and lots of in their editions. this is often the 1st computerized speech reputation booklet devoted to the deep studying strategy. as well as the rigorous mathematical remedy of the topic, the booklet additionally provides insights and theoretical beginning of a chain of hugely profitable deep studying models.
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Extra resources for Automatic speech recognition. A deep learning approach
Convergence of the EM algorithm is guaranteed (under mild conditions) in the sense that the average log-likelihood of the complete data does not decrease at each iteration, that is Q(θ|θk+1 ) ≥ Q(θ|θk ) with equality when θk is already an maximum-likelihood estimate. 4 EM Algorithm and Its Application to Learning HMM Parameters 35 The main properties of the EM algorithm are: • It gives only a local, rather than the global, optimum in the likelihood of partially observed data. • An initial value for the unknown parameter is needed, and as with most iterative procedures a good initial estimate is required for desirable convergence and a good maximum-likelihood estimate.
T . 11) rt (Σ i ) = N (0, Σ i ) is a zero-mean, Gaussian, IID (independent and identically distributed) residual sequence, which is generally state dependent as indexed by i. , not time-varying) given state i, their sum, which gives the observation ot is thus also IID given the state. Therefore, the HMM discussed above would produce locally or piecewise stationary sequences. Since the temporal locality in question is confined within state occupation of the HMM, we sometimes use the term stationary-state HMM to explicitly denote such a property.
Production models as a structural basis for automatic speech recognition. Speech Commun. 33(2–3), 93–111 (1997) 18. : A Markov model containing state-conditioned second-order nonstationarity: application to speech recognition. Comput. Speech Lang. 9(1), 63–86 (1995) 19. : Distributed speech processing in mipad’s multimodal user interface. IEEE Trans. Audio Speech Lang. Process. 20(9), 2409–2419 (2012) 20. : Dynamics of Speech Production and Perception. IOS Press, Washington (2006) 21. : Algonquin: iterating laplaces method to remove multiple types of acoustic distortion for robust speech recognition.
Automatic speech recognition. A deep learning approach by Dong Yu