TY - GEN
T1 - An articulation training system with intelligent interface and multimode feedbacks to articulation disorders
AU - Chen, Yeou Jiunn
AU - Wu, Jiunn-Liang
AU - Yang, Hui Mei
AU - Wu, Chung-Hsien
AU - Chen, Chih Chang
AU - Ju, Shan Shan
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Articulation training with many kinds of stimulus and messages such as visual, voice, and articulatory information can teach user to pronounce correctly and improve user's articulatory ability. In this paper, an articulation training system with intelligent interface and multimode feedbacks is proposed to improve the performance of articulation training. Dependent network is designed to model clinical knowledge of speech-language pathologists used in speech evaluation Automatic speech recognition with dependent network is then apply to identify the pronunciation errors. Besides, hierarchical Bayesian network is proposed to recognize user's emotion from speeches. With the information of pronunciation errors and user's emotion, the articulation training sentences can be dynamically selected. Finally, a 3D facial animation is provided to teach users to pronounce a sentence by using speech, lip motion, and tongue motion. Experimental results reveal the usefulness of proposed method and system.
AB - Articulation training with many kinds of stimulus and messages such as visual, voice, and articulatory information can teach user to pronounce correctly and improve user's articulatory ability. In this paper, an articulation training system with intelligent interface and multimode feedbacks is proposed to improve the performance of articulation training. Dependent network is designed to model clinical knowledge of speech-language pathologists used in speech evaluation Automatic speech recognition with dependent network is then apply to identify the pronunciation errors. Besides, hierarchical Bayesian network is proposed to recognize user's emotion from speeches. With the information of pronunciation errors and user's emotion, the articulation training sentences can be dynamically selected. Finally, a 3D facial animation is provided to teach users to pronounce a sentence by using speech, lip motion, and tongue motion. Experimental results reveal the usefulness of proposed method and system.
UR - http://www.scopus.com/inward/record.url?scp=77950871715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950871715&partnerID=8YFLogxK
U2 - 10.1109/IALP.2009.10
DO - 10.1109/IALP.2009.10
M3 - Conference contribution
AN - SCOPUS:77950871715
SN - 9780769539041
T3 - 2009 International Conference on Asian Language Processing: Recent Advances in Asian Language Processing, IALP 2009
SP - 3
EP - 6
BT - 2009 International Conference on Asian Language Processing
T2 - 2009 International Conference on Asian Language Processing: Recent Advances in Asian Language Processing, IALP 2009
Y2 - 7 December 2009 through 9 December 2009
ER -