A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of sp...A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological articulatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is approximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks （GRNN）. The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accuracy both acoustically and articulatorily.展开更多
基金Supported partly by the Promoting Science and Technology by the Japan Ministry of Education,Culture,Sports,Science and Technology and the SCOPE of the Ministry of Internal Affairs and Communications (MIC),Japan (No.071705001)
文摘A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological articulatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is approximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks （GRNN）. The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accuracy both acoustically and articulatorily.
