A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Elements of information theory
Elements of information theory
Neuro-fuzzy clustering of radiographic tibia image data using type 2 fuzzy sets
Information Sciences—Applications: An International Journal
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern recognition using type-II fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
Image thresholding using type II fuzzy sets
Pattern Recognition
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
MPEG VBR video traffic modeling and classification using fuzzy technique
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
Type-2 fuzzy hidden Markov models and their application to speech recognition
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Type-2 Fuzzy Markov Random Fields and Their Application to Handwritten Chinese Character Recognition
IEEE Transactions on Fuzzy Systems
Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation
IEEE Transactions on Image Processing
Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
Journal of Visual Languages and Computing
Multivalued Background/Foreground Separation for Moving Object Detection
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Information Sciences: an International Journal
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
SCS: Signal, Context, and Structure Features for Genome-Wide Human Promoter Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pattern Recognition
Image segmentation based on histogram analysis utilizing the cloud model
Computers & Mathematics with Applications
A hybrid GMM speaker verification system for mobile devices in variable environments
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A 2uFunction representation for non-uniform type-2 fuzzy sets: Theory and design
International Journal of Approximate Reasoning
Overview of Type-2 Fuzzy Logic Systems
International Journal of Fuzzy System Applications
Hi-index | 0.01 |
This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.