서지주요정보
Vector quantization과 hidden markov modeling을 이용한 한국어 무성음 인식에 관한 연구 = A study on korean unvoiced phoneme recognition based on vector quantization and hidden markov modeling
서명 / 저자 Vector quantization과 hidden markov modeling을 이용한 한국어 무성음 인식에 관한 연구 = A study on korean unvoiced phoneme recognition based on vector quantization and hidden markov modeling / 이민규.
저자명 이민규 ; Lee, Min-Gyu
발행사항 [서울 : 한국과학기술원, 1988].
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4104837

소장위치/청구기호

학술문화관(문화관) 보존서고

MEE 8853

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초록정보

In this thesis work, an unvoiced Korean phoneme recognition algorithm is described as a pre-processing stage for continuous Korean speech recognition. The proposed phoneme recognition algorithm consists of two processes: the/voiced/unvoiced/silence (V/UV/S) detection, and the phoneme segmentation and labeling. Input speech signal is first segmented into V/UV/S intervals using a pattern matching method based on the statistical decision theory. From the segmented V/UV/S intervals, the extracted unvoiced speech signal intervals are labeled to make unvoiced phoneme sequences. For the phoneme-level segmentation and labeling, we used vector quantizatio n(VQ) and hidden Markov modeling (HMM). Computer simulation has been done to obtain the performance of the proposed unvoiced Korean phoneme recognition algorithm using 200 word vocabularies consisting of names of universities, hospitals, and public offices in Seoul. The vocabularies are spoken by 6 male speakers under an ordinary ambient condition. Simulation results show that unvoiced interval detection error rates in the /V/UV/S detection process are about 2.4% and 30% when unvoiced phonemes occur at the first syllables of spoken words or not, respectively. VQ and HMM are then applied to detected unvoiced speech segments to recognize unvoiced phonemes in the signal segments. When 4 HMM states and 128 VQ codewords are used, and considering top 2 candidates, the unvoiced phoneme recognition accuracy about 73.4% is obtained. And when the transition part between unvoiced and voiced interval is included in the unvoiced interval, the result shows about 82% recognition accuracy with the same condition as above.

서지기타정보

서지기타정보
청구기호 {MEE 8853
형태사항 iv, 71 p. : 삽도 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Min-Gyu Lee
지도교수의 한글표기 : 이황수
지도교수의 영문표기 : Hwang-Soo Lee
학위논문 학위논문(석사) - 한국과학기술원 : 전기 및 전자공학과,
서지주기 참고문헌 : p. 65-69
주제 Speech perception.
Markov processes.
Phonemics.
Korean language.
Markov 과정. --과학기술용어시소러스
음성 인식. --과학기술용어시소러스
벡터 양자화. --과학기술용어시소러스
음소. --과학기술용어시소러스
Vector processing (Computer science)
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