Download e-book for kindle: Automatic speech recognition. A deep learning approach by Dong Yu

By Dong Yu

ISBN-10: 1447157788

ISBN-13: 9781447157786

ISBN-10: 1447157796

ISBN-13: 9781447157793

This booklet offers a entire assessment of the hot development within the box of automated speech acceptance with a spotlight on deep studying types together with deep neural networks and plenty of in their variations. this is often the 1st computerized speech acceptance e-book devoted to the deep studying strategy. as well as the rigorous mathematical remedy of the topic, the publication additionally offers insights and theoretical beginning of a chain of hugely profitable deep studying models.

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IEEE Trans. Audio, Speech Lang. Process. 20(1), 30–42 (2012) 4. : Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Process. 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.

IEEE ASSP Mag. 3(1), 4–16 (1986) 19. : Fundamentals of Speech Recognition. Prentice-Hall, Upper Saddle River (1993) 20. : Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986) 21. : Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 24–29 (2011) References 9 22. : Conversational speech transcription using context-dependent deep neural networks.

N , subject to the constraint Nj=1 ai j = 1. 41) where ξt (i, j) and γt (i) are computed according to Eqs. 39. To derive the reestimation formulas for the parameters in the state-dependent Gaussian distributions, we first remove optimization-independent terms and factors in Q 1 in Eq. 36. Then we have an equivalent objective function of N Tr Q 1 (μ i , Σ i ) = γt (i) ot − μi T Σ i−1 ot − μi − i=1 t=1 1 log |Σ i |. 43) for i = 1, 2, . . , N . For solving it, we employ the trick of variable transformation: K = Σ −1 (we omit the state index i for simplicity), and we treat Q 1 as a function of K.

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Automatic speech recognition. A deep learning approach by Dong Yu

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