主讲题目：A deep transfer learning approch for improved post-traumatic stress disorder diagnosis

时  间：2018年1月5日（周五）下午15:00

地  点：创新中心5楼005会议室

主讲人：美国老道明大学 李江（Jiang Li）副教授

主讲内容：

Post-traumatic stress disorder (PTSD) is a traumatic-stressor related disorder developed by exposure to a traumatic or adverse environmental event that caused serious harm or injury. Structured interview is the only widely accepted clinical practice for PTSD diagnosis but suffers from several limitations including the stigma associated with the disease. Diagnosis of PTSD patients by analyzing speech signals has been investigated as an alternative since recent years, where speech signals are processed to extract frequency features and these features are then fed into a classification model for PTSD diagnosis. In this paper, we developed a deep belief network (DBN) model combined with a transfer learning (TL) strategy for PTSD diagnosis. We computed three categories of speech features and utilized the DBN model to fuse these features. The TL strategy was utilized to transfer knowledge learned from a large speech recognition database, TIMIT, for PTSD detection where PTSD patient data is difficult to collect. We evaluated the proposed methods on two PTSD speech databases, each of which consists of audio recordings from 26 patients.

主讲人简介：

李江，现任美国Old Dominion University（老道明大学）电子与计算机工程系副教授。李教授于1992年，2000年和2004年分别毕业于上海交通大学，清华大学和得克萨斯大学阿灵顿分校获得学士，硕士和博士学位。曾于2004年到2006年在美国国立卫生院（National Institutes of Health）从事博士后研究工作。2007年春季加入老道明大学，并于2012年获得终身教授职位。其研究兴趣包括机器学习，数据挖掘，遥感图像处理，计算机辅助检测，并在这些相关领域发表了20多篇英文期刊文章和60多篇英文会议论文，近5年H指数11。他是IEEE高级会员，并于2007年曾被国际知名期刊Medical Physics聘为客座编委。