Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis

2013 ◽  
Vol 21 (10) ◽  
pp. 2129-2139 ◽  
Author(s):  
Zhen-Hua Ling ◽  
Li Deng ◽  
Dong Yu
Keyword(s):  
Speech Synthesis ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Statistical Parametric Speech Synthesis ◽  
Parametric Speech Synthesis

scholarly journals Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines

2011 ◽  
Vol 23 (5) ◽  
pp. 1306-1319 ◽  
Author(s):  
Guido Montufar ◽  
Nihat Ay
Keyword(s):  
Universal Approximation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Binary Vectors ◽  
Boltzmann Machines ◽  
Support Set ◽  
Universal Approximators

We improve recently published results about resources of restricted Boltzmann machines (RBM) and deep belief networks (DBN) required to make them universal approximators. We show that any distribution [Formula: see text] on the set [Formula: see text] of binary vectors of length [Formula: see text] can be arbitrarily well approximated by an RBM with [Formula: see text] hidden units, where [Formula: see text] is the minimal number of pairs of binary vectors differing in only one entry such that their union contains the support set of [Formula: see text]. In important cases this number is half the cardinality of the support set of [Formula: see text] (given in Le Roux & Bengio, 2008 ). We construct a DBN with [Formula: see text], hidden layers of width [Formula: see text] that is capable of approximating any distribution on [Formula: see text] arbitrarily well. This confirms a conjecture presented in Le Roux and Bengio ( 2010 ).

Sign Language Fingerspelling Recognition Using Depth Information and Deep Belief Networks

2018 ◽  
Vol 32 (06) ◽  
pp. 1850018 ◽  
Author(s):  
Yong Hu ◽  
Hai-Feng Zhao ◽  
Zhi-Gang Wang
Keyword(s):  
Sign Language ◽  
Deep Belief Network ◽  
Depth Information ◽  
Histogram Of Oriented Gradients ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Belief Network ◽  
Average Accuracy

In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.

scholarly journals Robustness of hardware-oriented restricted Boltzmann machines in deep belief networks for reliable processing

2016 ◽  
Vol 7 (3) ◽  
pp. 395-406 ◽  
Author(s):  
Kodai Ueyoshi ◽  
Takao Marukame ◽  
Tetsuya Asai ◽  
Masato Motomura ◽  
Alexandre Schmid
Keyword(s):  
Belief Networks ◽  
Deep Belief Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines
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