Efficient Continuous Vocabulary Clustering Modeling forTying Model Recognition Performance Improvement

안찬식; 오상엽

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2010, 15(1), pp.177-183
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

안찬식 ¹, 오상엽 ²

¹광운대학교
²경원대학교

ABSTRACT

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%.
KEYWORDS

clustering modeling, phoneme modeling, tying model, gaussian model, vocabulary recognition
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KCI Citation (14)

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