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 Cerebellum-like Neural Network for Short-range Timing Function of a Robotic Speaking System
Tác giả hoặc Nhóm tác giả: Thanh Vo Nhu and Hideyuki Sawada,
Nơi đăng: 2017 The 3rd International Conference on Control, Automation and Robotics (ICCAR 2017) Nagoya, Japan. ISBN: 978-1-5090-6086 -3, Scopus Indexed; Số: 3;Từ->đến trang: 184-187;Năm: 2017
Lĩnh vực: Khoa học công nghệ; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
The timing control is necessary for determining its duration, stress, and rhythm in human speech; however, little attention has been paid to these issues when building a speech synthesis system. We have developed a talking robot, which generates human-like vocal sounds. The cerebellum is an important part of human brain organ that has a significant role in the coordination, precision, and timing of motor responses. In this study, we develop a simplified cerebellum-like spiking neural network model to control the timing function for the talking robot. The model was designed using the System Generator software in Matlab, and the timing duration of trained speech was estimated using hardware cosimulated with a field programmable gate array board (FPGA). The timing information obtained from the co-simulation, together with the output motor vector, is sent to the talking robot controller to generate a sound with a short duration. The result indicates that this model can be used for short-range timing learning of the talking robot.
ABSTRACT
The timing control is necessary for determining its duration, stress, and rhythm in human speech; however, little attention has been paid to these issues when building a speech synthesis system. We have developed a talking robot, which generates human-like vocal sounds. The cerebellum is an important part of human brain organ that has a significant role in the coordination, precision, and timing of motor responses. In this study, we develop a simplified cerebellum-like spiking neural network model to control the timing function for the talking robot. The model was designed using the System Generator software in Matlab, and the timing duration of trained speech was estimated using hardware cosimulated with a field programmable gate array board (FPGA). The timing information obtained from the co-simulation, together with the output motor vector, is sent to the talking robot controller to generate a sound with a short duration. The result indicates that this model can be used for short-range timing learning of the talking robot.
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