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
A two-stage algorithm for one-microphone reverberant speech enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Discrimination of speech from nonspeech based on multiscale spectro-temporal Modulations
IEEE Transactions on Audio, Speech, and Language Processing
Speech Analysis in a Model of the Central Auditory System
IEEE Transactions on Audio, Speech, and Language Processing
A two stage algorithm for K-mode convolutive nonnegative tucker decomposition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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How to extract robust feature is an important research topic in machine learning community. In this paper, we investigate robust feature extraction for speech signal based on tensor structure and develop a new method called constrained Nonnegative Tensor Factorization (cNTF). A novel feature extraction framework based on the cortical representation in primary auditory cortex (AI) is proposed for robust speaker recognition. Motivated by the neural firing rates model in AI, the speech signal first is represented as a general higher order tensor. cNTF is used to learn the basis functions from multiple interrelated feature subspaces and find a robust sparse representation for speech signal. Computer simulations are given to evaluate the performance of our method and comparisons with existing speaker recognition methods are also provided. The experimental results demonstrate that the proposed method achieves higher recognition accuracy in noisy environment.