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Title: Type-2 fuzzy Hidden Markov Models to phoneme recognition
Authors: Zeng, Jia (曾嘉)
Liu, Zhi-qiang
Department: School of Creative Media (Zeng, J.; Prof. Liu, Z. Q.)
Issue Date: 2005
Award: Won the Second Prize (Postgraduate Section) in the IEEE Hong Kong Section Student Paper Contest 2004.
Type: Article
Abstract: This paper presents a novel extension of Hidden Markov Models (HMMs): type-2 fuzzy HMMs (type-2 FHMMs). The advantage of this extension is that it can handle both randomness and fuzziness within the framework of type-2 fuzzy sets (FSs) and fuzzy logic systems (FLSs). Membership functions (MFs) of type-2 fuzzy sets are three-dimensional. It is the third dimension that provides the additional degrees of freedom that make it possible to handle both uncertainties. We apply the type-2 FHMM as acoustic models for phoneme recognition on TIMIT speech database. Experimental results show that the type-2 FHMM has a comparable performance as that of the HMM but is more robust to noise, while it retains almost the same computational complexity as that of the HMM.
Remarks: The Institutional Repository only contains the News announcement
Appears in Collections:Student Works With External Awards

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