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DC Field | Value | Language |
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dc.contributor.author | Zeng, Jia (曾嘉) | |
dc.contributor.author | Liu, Zhi-qiang | |
dc.date.accessioned | 2005-04-13T07:43:14Z | |
dc.date.accessioned | 2017-09-19T09:18:33Z | |
dc.date.accessioned | 2019-02-12T08:43:43Z | - |
dc.date.available | 2005-04-13T07:43:14Z | |
dc.date.available | 2017-09-19T09:18:33Z | |
dc.date.available | 2019-02-12T08:43:43Z | - |
dc.date.issued | 2005 | |
dc.identifier.other | scm2005-001 | |
dc.identifier.uri | http://144.214.8.231/handle/2031/260 | - |
dc.description.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. | |
dc.format.extent | 152 bytes | |
dc.format.mimetype | text/html | |
dc.language.iso | en_US | |
dc.rights | This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner. | |
dc.title | Type-2 fuzzy Hidden Markov Models to phoneme recognition | en |
dc.type | Article | en |
dc.contributor.department | School of Creative Media (Zeng, J.; Prof. Liu, Z. Q.) | |
dc.description.award | Won the Second Prize (Postgraduate Section) in the IEEE Hong Kong Section Student Paper Contest 2004. | |
dc.description.remark | The Institutional Repository only contains the News announcement | |
Appears in Collections: | Student Works With External Awards |
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award_news.html | 152 B | HTML | View/Open |
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