Fundamentals of speech recognition
Fundamentals of speech recognition
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
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
Efficient Coding of Time-Relative Structure Using Spikes
Neural Computation
Sparse representations of polyphonic music
Signal Processing - Sparse approximations in signal and image processing
Sparse spectrotemporal coding of sounds
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A two-stage algorithm for one-microphone reverberant speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
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This paper investigates the problem of speaker recognition in noisy conditions. A new approach called nonnegative tensor principal component analysis (NTPCA) with sparse constraint is proposed for speech feature extraction. We encode speech as a general higher-order tensor in order to extract discriminative features in spectrotemporal domain. Firstly, speech signals are represented by cochlear feature based on frequency selectivity characteristics at basilar membrane and inner hair cells; then, low-dimension sparse features are extracted by NTPCA for robust speaker modeling. The useful information of each subspace in the higher-order tensor can be preserved. Alternating projection algorithm is used to obtain a stable solution. Experimental results demonstrate that our method can increase the recognition accuracy specifically in noisy environments.