TY - GEN
T1 - Interval type-2 fuzzy hidden markov models
AU - Zeng, Jia
AU - Liu, Zhi-Qiang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2004
Y1 - 2004
N2 - This paper presents an extension of Hidden Markov Models (HMMs) using interval type-2 fuzzy sets (FSs) and fuzzy logic systems (FLSs) to produce interval type-2 FHMMs. The advantage of this extension is that it can handle both randomness and fuzziness. Membership function (MF) of the type-2 FS is three-dimensional. It is the third-dimension that provides additional degrees of freedom to evaluate HMM's uncertainties. An attractive property of this extension is that if all uncertainties disappear, the interval type-2 FHMM reduces to the classical HMM. We apply our interval type-2 FHMM as acoustic model 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 the speech variation, while it retains almost the same computational complexity as that of the HMM.
AB - This paper presents an extension of Hidden Markov Models (HMMs) using interval type-2 fuzzy sets (FSs) and fuzzy logic systems (FLSs) to produce interval type-2 FHMMs. The advantage of this extension is that it can handle both randomness and fuzziness. Membership function (MF) of the type-2 FS is three-dimensional. It is the third-dimension that provides additional degrees of freedom to evaluate HMM's uncertainties. An attractive property of this extension is that if all uncertainties disappear, the interval type-2 FHMM reduces to the classical HMM. We apply our interval type-2 FHMM as acoustic model 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 the speech variation, while it retains almost the same computational complexity as that of the HMM.
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U2 - 10.1109/FUZZY.2004.1375569
DO - 10.1109/FUZZY.2004.1375569
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780383532
VL - 2
T3 - IEEE International Conference on Fuzzy Systems
SP - 1123
EP - 1128
BT - 2004 IEEE International Conference on Fuzzy Systems - Proceedings
PB - IEEE
T2 - 2004 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2004
Y2 - 25 July 2004 through 29 July 2004
ER -