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Please use this identifier to cite or link to this item: http://hdl.handle.net/2031/305

Title: Type-2 fuzzy Hidden Markov Models and their applications to phoneme classification
Authors: Zeng, Jia (曾嘉)
Department: School of Creative Media
Issue Date: Aug-2005
Award: Won the First Prize (Postgraduate Section) in the IEEE Region 10 Student Paper Contest 2005.
Supervisor: Prof. Liu, Zhi-Qiang
Type: Article
Abstract: 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 HMM's 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. 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.
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Appears in Collections:Student Works With External Awards

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