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On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering
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Information Sciences: an International Journal
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Information Sciences: an International Journal
On constructing parsimonious type-2 fuzzy logic systems via influential rule selection
IEEE Transactions on Fuzzy Systems
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
Modeling uncertain speech sequences using type-2 fuzzy hidden Markov models
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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IEEE Transactions on Fuzzy Systems - Special section on computing with words
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Uncertainty measures for general Type-2 fuzzy sets
Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
Similarity, inclusion and entropy measures between type-2 fuzzy sets based on the Sugeno integral
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On interval type-2 rough fuzzy sets
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Information Sciences: an International Journal
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International Journal of Approximate Reasoning
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This paper presents an extension of hidden Markov models (HMMs) based on the type-2 (T2) fuzzy set (FS) referred to as type-2 fuzzy HMMs (T2 FHMMs). Membership functions (MFs) of T2 FSs are three-dimensional, and this new third dimension offers additional degrees of freedom to evaluate the HMMs fuzziness. Therefore, T2 FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. We derive the T2 fuzzy forward-backward algorithm and Viterbi algorithm using T2 FS operations. In order to investigate the effectiveness of T2 FHMMs, we apply them to phoneme classification and recognition on the TIMIT speech database. Experimental results show that T2 FHMMs can effectively handle noise and dialect uncertainties in speech signals besides a better classification performance than the classical HMMs.