CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
张大伟1; 杨明浩1,3; 陶建华1,2,3
Conference Name第十四届全国人机语音通讯学术会议 (NCMMSC 2017)
Conference Date2017-10-11~13
Conference Place中国连云港
Other AbstractArticulatory motion visualization is very important in research on human pronunciation mechanism, language teaching and pathological speech analysis. A text-independent speech-driven tongue motion synthesis method is proposed. Based on medical image data and the automatic extraction method, tongue can be synthesized by using combined deep neural network models. Moreover, tongue contour denoising, acoustic features selection and different mapping model structures are compared and analyzed. Experiments show that the proposed method can effectively solve the over or under fitting problems with limited noisy samples, and the accuracy of predicted tongue contours is obviously higher than baseline methods and even higher than the extracted contours in some key points.
Keyword舌位运动合成 语音驱动 医学影像 组合深度神经网络
Document Type会议论文
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
张大伟,杨明浩,陶建华. 基于医学影像的语音驱动舌位运动合成[C],2017.
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