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Structure Learning From Distributed Noisy Data
, M.Sc. Thesis Sharif University of Technology ; Motahari, Abolfazl (Supervisor) ; Manzuri Shalmani, Mohammad Taghi (Co-Supervisor)
Abstract
Probabilistic graphical models have great applications in studying and analyzing realworld data. For instance, these models have been used in reconstructing gene regularity networks. Specifically, learning the edges’ structure of graphical models is of great importance.Knowledge about the underlying structure of a graphical model brings about a valuable framework for the decomposition of the model’s distribution and reveals important information such as dependency among dimensions of samples, etc. Most existing methods for structure learning obtain the underlying structure of the model in a centralized fashion and without considering noise in data. In many applications, data exist in a...
Robust speech recognition using MLP neural network in log-spectral domain
, Article IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2009, 14 December 2009 through 16 December 2009, Ajman ; 2009 , Pages 467-472 ; 9781424459506 (ISBN) ; Sametit, H ; Razzazi, F ; BabaAli, B ; Dabbaghchiarr, S ; Sharif University of Technology
Abstract
In this paper, we have proposed an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. A Multi Layer Perceptron (MLP) neural network in the log spectral domain has been employed to minimize the difference between noisy and clean speech. By using this method, as a pre-processing stage of a speech recognition system, the recognition rate in noisy environments has been improved. We extended the application ofthe system to different environments with different noises without retraining HMMmodel. We trained the feature extraction stage with a small portion of noisy data which was created by...