By Dong Yu

ISBN-10: 1447157788

ISBN-13: 9781447157786

ISBN-10: 1447157796

ISBN-13: 9781447157793

This publication offers a finished evaluate of the new development within the box of computerized speech acceptance with a spotlight on deep studying versions together with deep neural networks and lots of in their versions. this can be the 1st computerized speech reputation publication devoted to the deep studying process. as well as the rigorous mathematical therapy of the topic, the ebook additionally offers insights and theoretical origin 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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Spoken Language Processing. Prentice Hall, Englewood Cliffs (2001) 24. : An overview of modern speech recognition. J. ) Handbook of Natural Language Processing, 2nd edn. CRC Press, Taylor and Francis Group, Boca Raton, FL (2010). ISBN 978-1420085921 25. : Discriminative learning in sequential pattern recognition—a unifying review for optimization-oriented speech recognition. IEEE Signal Process. Mag. 27(3), 115–127 (2010) 26. : Large margin hidden markov models for speech recognition. IEEE Trans.

Maximum likelihood estimation for mixture multivariate stochastic observations of markov chains. In: IEEE International Symposium on Information Theory vol. 32(2), pp. 307–309 (1986) 28. : Joint factor analysis of speaker and session variability: theory and algorithms. CRIM, Montreal, (Report) CRIM-06/08-13 (2005) 29. : Speech production knowledge in automatic speech recognition. J. Acoust. Soc. Am. 121, 723–742 (2007) 30. : A functional articulatory dynamic model for speech production. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol.

IEEE Signal Process. Mag. 25(3), 28–38 (2008) 25. : Automated directory assistance system-from theory to practice. In: Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 2709–2712 (2007) 26. : Personalizing model [M] for voice-search. In: Proceedings of Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 609–612 (2011) Part I Conventional Acoustic Models Chapter 2 Gaussian Mixture Models Abstract In this chapter we first introduce the basic concepts of random variables and the associated distributions.

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Automatic speech recognition. A deep learning approach by Dong Yu

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