Continuous speech recognition systems based on non-uniform unit neural network and a fuzzy expert system불균일 단위 신경회로망과 퍼지전문가 시스템에 기반한 연속음성인식 시스템

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 476
  • Download : 0
DC FieldValueLanguage
dc.contributor.advisorOh, Yung-Hwan-
dc.contributor.advisor오영환-
dc.contributor.authorYu, Ha-Jin-
dc.contributor.author유하진-
dc.date.accessioned2011-12-13T05:24:10Z-
dc.date.available2011-12-13T05:24:10Z-
dc.date.issued1997-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=114171&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/33087-
dc.description학위논문(박사) - 한국과학기술원 : 전산학과, 1997.2, [ ix, 133 p. ]-
dc.description.abstractIt is necessary for a computer to recognize continuous speech, to provide the most convenient way of communication for users. The major problem addressed in this study is the difficulty of segmenting continuous speech into primitive units for recognition. The exact segmentation of phonemes is almost impossible, especially when the speech is spoken without restrictions. Most of the recognizers search for optimal positions of the units during recognition by using dynamic programming or by shifting windows. Such processes usually take much more time than segmenting the speech before classification. As a solution, we define a non-uniform unit and propose a segmentation method for the unit. A unit is defined as a segment which is cut out at stationary points of the speech, and have a transition part in the middle of it. It is segmented by using spectral transition measure without iterations or exhaustive search. A unit can have an arbitrary number of phonemes so it can absorb co-articulation effects which span for several phonemes. To show the effectiveness of the unit, we implemente two recognition systems based on a knowledge-based and a connectionist approaches. In the knowledge-based system, the rules for recognizing units are represented by frames which describe the dynamic structures of the units. Then, fuzzy concepts are used for speech recognition in two ways. First, fuzzy reasoning is applied to the recognition of the basic unit. The second application of fuzzy concepts in this study is estimating fuzzy phoneme similarity relation for word spotting. We propose a method to evaluate the similarities of the pairs of Korean phonemes based on the similarities of the articulatory features. The similarities of the places and the manners of articulations of phoneme pairs are estimated and then the results are combined by using fuzzy operations to calculate the similarities of the phonemes. In the neural network system, the segmentation and classification of the ...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectNeural network-
dc.subjectNon-uniform unit-
dc.subjectContinuous speech recognition-
dc.subjectFuzzy expert system-
dc.subject퍼지전문가 시스템-
dc.subject신경회로망-
dc.subject불균일 단위-
dc.subject연속음성인식-
dc.titleContinuous speech recognition systems based on non-uniform unit neural network and a fuzzy expert system-
dc.title.alternative불균일 단위 신경회로망과 퍼지전문가 시스템에 기반한 연속음성인식 시스템-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN114171/325007-
dc.description.department한국과학기술원 : 전산학과, -
dc.identifier.uid000925221-
dc.contributor.localauthorOh, Yung-Hwan-
dc.contributor.localauthor오영환-
Appears in Collection
CS-Theses_Ph.D.(박사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0