Fundamentals of speech recognition
Fundamentals of speech recognition
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Pattern Recognition Letters
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Nonnegative features of spectro-temporal sounds for classification
Pattern Recognition Letters
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Applied Signal Processing
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Nonnegative tensor factorization is an extension of nonnegative matrix factorization(NMF) to a multilinear case, where nonnegative constraints are imposed on the PARAFAC/Tucker model. In this paper, to identify speaker from a noisy environment, we propose a new method based on PARAFAC model called constrained Nonnegative Tensor Factorization (cNTF). Speech signal is encoded as a general higher order tensor in order to learn the basis functions from multiple interrelated feature subspaces. We simulate a cochlear-like peripheral auditory stage which is motivated by the auditory perception mechanism of human being. A sparse speech feature representation is extracted by cNTF which is used for robust speaker modeling. Orthogonal and nonsmooth sparse control constraints are further imposed on the PARAFAC model in order to preserve the useful information of each feature subspace in the higher order tensor. Alternating projection algorithm is applied to obtain a stable solution. Experiments results demonstrate that our method can improve the recognition accuracy specifically in noise environment.