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초록

영어

This study attempted to link continuous and dynamic articulatory information to categorical phonological feature representations through a neural network model. The Long Short-Term Memory (LSTM) model was used in the current paper, which is a type of recurrent neural network including temporal information connections. The test results of the LSTM model mapping muscular activation into phonological features show that (i) gradient values of phonological features are derived from the degree of activation of the tongue muscles, which determines the movement and shape of the tongue, and (2) the LSTM model can systematically capture vowels' co-articulatory effect on consonants.

목차

Abstract
1. 서론
2. 조음 정보: 혀 근육 활성화 정도
2.1. 왜 혀 근육 활성화 정보를 음운 자질과 연결하는가?
2.2. 3D 혀 모델을 이용한 혀 근육 활성화 시뮬레이션
3. 조음 정보와 음운 자질의 연결: 신경망 모델
3.1. 모델 학습
3.2. 모델 테스트
4. 논의
4.1. 조음 정보의 음운 자질 표상: 범주 내 연속값
4.2. 시간적 변화와 동시 조음 효과 반영
5. 결론
참고문헌

키워드

음운 표상 자질 조음 정보 혀 근육 활성화 신경망 학습 장·단기 메모리 모델 phonological representation feature articulatory information activation of tongue muscles neural network long・short-term memory model

저자

• 장하연 [ Hayeun Jang | 부산외국어대학교/조교수 ]

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