By Dong Yu
This publication offers a complete evaluate 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 variations. this is often the 1st computerized speech acceptance e-book devoted to the deep studying process. as well as the rigorous mathematical therapy of the topic, the booklet additionally provides insights and theoretical beginning of a chain of hugely winning deep studying models.
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Additional resources for Automatic Speech Recognition: A Deep Learning Approach
Morgan and Claypool, New York (2006) 10. : Large vocabulary speech recognition under adverse acoustic environment. In: Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 806–809 (2000) 11. : A. Acero: recursive estimation of nonstationary noise using iterative stochastic approximation for robust speech recognition. IEEE Trans. Speech Audio Process. 11, 568–580 (2003) 12. : A Bayesian approach to speech feature enhancement using the dynamic cepstral prior. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol.
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. 13. 1 Introduction In the previous chapter, we reviewed aspects of probability theory and basic statistics, where we introduced the concept of random variables and the associated concept of probability distributions. We then discussed Gaussian and mixture-of-Gaussian random variables and their vector-valued or multivariate versions.
8), in contrast to the unimodal property of the Gaussian distribution where M = 1. This makes it possible for a mixture Gaussian distribution 16 2 Gaussian Mixture Models to adequately describe many types of physical data (including speech data) exhibiting multimodality poorly suited for a single Gaussian distribution. The multimodality in data may come from multiple underlying causes each being responsible for one particular mixture component in the distribution. If such causes are identified, then the mixture distribution can be decomposed into a set of cause-dependent or contextdependent component distributions.