| Abstract |
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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.
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 Additional Information
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Citation:
Jia Zeng, Zhi-Qiang Liu,
"Type-2 Fuzzy Hidden Markov Models to Phoneme Recognition,"
icpr,
pp. 192-195,
17th International Conference on Pattern Recognition (ICPR'04) - Volume 1,
2004
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