• Journal of Digital Convergence
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• v.11 no.11
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• pp.367-372
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• 2013

In general, Levenshtein distance algorithm have a problem with not distinguish the consideration of vacabulary retrieve, because Levenshtein methode is used to vocabulary order are not defined. In this paper, we propose a improved Levenshtein methode, it effectively manage the vocabulary retrieve by frequency use of a vocabulary, and it gives the weight number which have a order between vocabularies. Therefore proposed methode have a advantage of solve the defect of perception rate in the case of increase the vocabulary, improve the recognition time become higher and it can be effectively retrieval space management.. System performance as a result of represent vocabulary dependence recognition rate of 97.81%, vocabulary independence recognition rate of 96.91% in indoor environment. Also, vocabulary dependence recognition rate of 91.11%, vocabulary independence recognition rate of 90.01% in outdoor environment.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1436-1443
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• 2010

In vocabulary recognition system has it's bad points of processing vocabulary unseen triphone and then no got distribution of confidence measure by cannot normalization. According to this problem to improve suggested VCOR(Version Control for Out-of Rejection) system by out-of vocabulary rejection algorithm use vocabulary management optimization and then phone data search support. In VCOR system to provide vocabulary information efficiently offering for user's vocabulary information using extend facet classification that improved for vocabulary measure management function offering accuracy of recognition for vocabulary. In this paper proposed system performance as a result of represent vocabulary dependence recognition rate of 97.56%, vocabulary independence recognition rate of 96.23%.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.217-223
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• 2009

Vocabulary recognition system of Mobile terminal is executed statistical method for vocabulary recognition and used statistical grammar recognition system using N-gram. If limit arithmetic processing capacity in memory of vocabulary to grow then vocabulary recognition algorithm complicated and need a large scale search space and many processing time on account of impossible to process. This study suggest vocabulary recognition optimize using MLHF System. MLHF separate acoustic search and lexical search system using FLaVoR. Acoustic search feature vector of speech signal extract using HMM, lexical search recognition execution using Levenshtein distance algorithm. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%, represent recognition speed of 1.61 second.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.367-372
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• 2013

In Speech recognition system, there is a problem with phoneme in the model training and it cause a stored mode regeneration process which come into being appear time and more costs. In this paper, we propose the methode of likely phoneme model schema using decision tree clustering. Proposed system has a robust and correct sound model which system apply the decision tree clustering methode form generate model, therefore this system reduce the regeneration process and provide a retrieve the phoneme unit in probability model. Also, this proposed system provide a additional likely phoneme model and configured robust correct sound model. System performance as a result of represent vocabulary dependence recognition rate of 98.3%, vocabulary independence recognition rate of 98.4%.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.177-183
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• 2010

In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.496-503
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• 2010

Speech recognition system is developed of standalone, In case of a mobile terminal using that low recognition rate represent because of limitation of memory size and audio compression. This study suggest vocabulary recognition highest performance improvement system for separate acoustic search and lexical search. Acoustic search is carry out in mobile terminal, lexical search is carry out in server processing system. feature vector of speech signal extract using GMM a phoneme execution, recognition a phoneme list transmission server using Lexical Tree Search algorithm lexical search recognition execution. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.71%, represent recognition speed of 1.58 second.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.127-134
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• 2010

In continuous vocabulary recognition system by probability distribution of clustering method has used model parameters of an advance estimate to generated each contexts for phoneme data surely needed but it has it's bad points of gaussian model the accuracy unsecure of composed model for phoneme data. To improve suggested probability distribution mixed gaussian model to optimized that phoneme data search supported configuration thread system. This paper of configuration thread system has used extension facet classification user phoneme configuration thread information offered gaussian model the accuracy secure. System performance as a result of represent vocabulary dependence recognition rate of 98.31%, vocabulary independence recognition rate of 97.63%.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.273-291
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• 2019

This study aims to extend fundamental categories from Clause 11, "facet analysis" in International Standards for thesaurus(ISO 25964-1) by analyzing fundamental categories of Clause 11 and concept and their scope in a thesaurus of Clause 5. For to do this, the fundamental categories were established by adjusting partially and adding mental entities explicitly referencing the highest concepts(YAMATO which is the upper ontology of Mizoguchi, and ISO 2788) and existing fundamental categories(PMEST, FRBR group 3 entities, 13 categories in CRG). Also, established fundamental categories were reorganized and structured based on concreteness/abstraction of PMEST in Ranganathan and independence/dependence of YAMATO in Mizoguchi. And the upper categories were divided into independent and dependent entities. Under these entities 28 criteria are included in the independent ones and 2 criteria in the dependent ones. In the further study, the result of this study can be expected to reuse and refer as controlled vocabulary in the field like classification, taxonomies and thesauri where expected to utilize fundamental categories and as the high-level concept when constructing an ontology for information retrieval.