Graphical model architectures for speech recognition

By J. Bilmes, C. Bartels. 2005

132 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Deep Learning: Methods and Applications (2014, background): “The above deepstructured generative models of speech can be shown as special cases of the more general dynamic network model and even more general dynamic graphical models [35, 34].”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “The above deep-structured generative models of speech can be shown as special cases of the more general dynamic Bayesian network model and even more general dynamic graphical models [77, 78].”
Investigation of full-sequence training of deep belief networks for speech recognition (2010, background): “Research in speech recognition has explored layered architectures in the recognizer design for quite some time (e.g., [1][2][3][5][15][17][18][19][20]), motivated partly by the desire to capitalize on some analogous properties in the human speech generation and perception systems.”
Structured speech modeling (2006, background): “…in the field understand the fragile nature of the current speech recognition system design, and have advocated that new, serious research is needed to overcome some fundamental limitations of the current speech recognition technology (e.g., [3], [6], [9], [29], [34], [40], [42], [49], [53], [59]).”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “Overviews on the general use of dynamic Bayesian networks, which belong to directed form of graphical models, for ASR have been provided in [4], [57], [58].”
An Overview of Deep-Structured Learning for Information Processing (2011, background): “All the above deep-structured generative models of speech can be shown as special cases of the more general dynamic Bayesian network model and even more general dynamic graphical models ( Bilmes and Bartels , 2005; Bilmes, 2010).”
Dynamic Speech Models: Theory, Algorithms, and Applications (2006, methodology): “The model is formulated in specially structured DBN, in which the speech dynamics at separate levels of the generative chain are represented by distinct (but dependent) discrete and continuous state variables and by their characteristic temporal evolution.”
Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling (2014, methodology): “Multi-stream HMM systems were originally proposed to combine independent feature streams [17].”