한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제10권2호
  • /
  • Pages.159-165
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  • 2019
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  • 2233-4890(pISSN)
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  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
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형태소 분석기를 이용한 키워드 검색 기반 한국어 텍스트 명령 시스템

Keyword Retrieval-Based Korean Text Command System Using Morphological Analyzer

  • 박대근 (공주대학교 게임디자인학과) ;
  • 이완복 (공주대학교 게임디자인학과)
  • Park, Dae-Geun (Department of Game Design, Kongju National University) ;
  • Lee, Wan-Bok (Department of Game Design, Kongju National University)
  • 투고 : 2018.11.20
  • 심사 : 2019.02.20
  • 발행 : 2019.02.28
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초록

딥러닝을 기반으로 한 음성 인식 기술이 상용 제품에 적용되기 시작했지만, 음성 인식으로 분석된 텍스트를 효율적으로 처리할 방법이 없기 때문에 VR 컨텐츠에서 그 적용 예를 찾아 보기는 쉽지 않다. 본 논문은 문장의 형태소를 분석하는 형태소 분석기와 챗봇 개발에 주로 이용되는 검색 기반 모델(Retrieval-Based Model)을 활용하여 명령어를 효율적으로 인식하고 대응할 수 있는 한국어 텍스트 명령 시스템을 제안하는 것을 목적으로 한다. 실험 결과 제안한 시스템은 문자열 비교 방식과 같은 동작을 하기 위해 16%의 명령어만 필요했으며, Google Cloud Speech와 연동하였을 때 60.1%의 성공률을 보였다. 실험 결과를 통해 제안한 시스템이 문자열 비교 방식보다 효율적이라는 것을 알 수 있다.

Based on deep learning technology, speech recognition method has began to be applied to commercial products, but it is still difficult to be used in the area of VR contents, since there is no easy and efficient way to process the recognized text after the speech recognition module. In this paper, we propose a Korean Language Command System, which can efficiently recognize and respond to Korean speech commands. The system consists of two components. One is a morphological analyzer to analyze sentence morphemes and the other is a retrieval based model which is usually used to develop a chatbot system. Experimental results shows that the proposed system requires only 16% commands to achieve the same level of performance when compared with the conventional string comparison method. Furthermore, when working with Google Cloud Speech module, it revealed 60.1% of success rate. Experimental results show that the proposed system is more efficient than the conventional string comparison method.

키워드

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Fig. 1. Market Outlook

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Fig. 2. The process of registering a skill

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Fig. 3. Flowchart of Korean text command system

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Fig. 4. Flowchart of Korean text command system

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Fig. 5. Comparison of dictionary design methods

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Fig. 6. Postpositions

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Fig. 7. Keyword extraction result

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Fig. 8. Command search process flowchart

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Fig. 9. Command Search Result

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Fig. 10. Action pseudocode

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Fig. 11. Unity3D 2017.4.2.f2

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Fig. 12. Action pseudocode

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Fig. 13. Recognized command and registered command

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