Journal of IKEEE (전기전자학회논문지)
- Volume 11 Issue 2
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- Pages.89-94
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- 2007
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- 1226-7244(pISSN)
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- 2288-243X(eISSN)
The Study of Korean Speech Recognition for Various Continue HMM
다양한 연속밀도 함수를 갖는 HMM에 대한 우리말 음성인식에 관한 연구
- Woo, In-Sung (Dept. of Computer Engineering, Hoseo University) ;
- Shin, Chwa-Cheul (Dept. of Computer Engineering, Hoseo University) ;
- Kang, Heung-Soon (Dept. of Computer Engineering, Hoseo University) ;
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Kim, Suk-Dong
(Dept. of Computer Engineering, Hoseo University)
- Published : 2007.06.29
Abstract
This paper is a study on continuous speech recognition in the Korean language using HMM-based models with continuous density functions. Here, we propose the most efficient method of continuous speech recognition for the Korean language under the condition of a continuous HMM model with 2 to 44 density functions. Two voice models were used CI-Model that uses 36 uni-phones and CD-Model that uses 3,000 tri-phones. Language model was based on N-gram. Using these models, 500 sentences and 6,486 words under speaker-independent condition were processed. In the case of the CI-Model, the maximum word recognition rate was 94.4% and sentence recognition rate was 64.6%. For the CD-Model, word recognition rate was 98.2% and sentence recognition rate was 73.6%. The recognition rate of CD-Model we obtained was stable.
본 논문은 연속 밀도 함수를 갖는 HMM별 한국어 연속 음성인식에 관한 연구이다. 여기서 우리는 밀도 함수가 2개에서 44개까지 갖는 연속 HMM모델에서 가장 효율적인 연속 음성인식을 위한 방법을 제시한다. 음성 모델은 36개로 구성한 기본음소를 사용한 CI-Model과 3,000개로 구성한 확장음소를 사용한 CD-Model을 사용하였고, 언어 모델은 N-gram을 이용하여 처리하였다. 이 방법을 사용하여 500개의 문장과 6,486개의 단어에 대하여 화자 독립으로 CI Model에서 최고 94.4%의 단어인식률과 64.6%의 문장인식률을 얻었고, CD Model에서는98.2%의 단어인식률과 73.6%의 문장인식률을 안정적으로 얻었다.