ID：170 899 0812
goal of SE is to enhance the speech signals by reducing distortions caused by additive and convoluted noises in order to achieving improved human-human and human-machine communication efficacy. In the this talk, we will review the system architecture and fundamental theories of deep learning based SE approaches. Next, we will present more recent advances, including end-to-end and goal-driven based SE systems as well as the SE systems with improved architectures and feature extraction procedure. The reinforcement learning and generative adversarial network (GAN)-based SE methods will also be presented. Finally, we will discuss some applications based on the deep learning SE systems, including impaired speech transformation and noise reduction for assistive hearing devices.
Yu Tsao received the B.S. and M.S. degrees in electrical engineering from National Taiwan University, Taipei, Taiwan, in 1999 and 2001, respectively, and the Ph.D. degree in electrical and computer engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2008. From 2009 to 2011, he was a Researcher with the National Institute of Information and Communications Technology, Tokyo, Japan, where he engaged in research and product development in automatic speech recognition for multilingual speech-to-speech translation. He is currently an Associate Research Fellow with the Research Center for Information Technology Innovation, Academia Sinica, Taipei. His research interests include speech and speaker recognition, acoustic and language modeling, audio coding, and bio-signal processing. He is currently an Associate Editor for the IEEE/ACM Transactions on Audio, Speech, and Language Processing and IEICE Transactions on Information and Systems and a Distinguished Lecturer of APSIPA. He was the recipient of the Academia Sinica Career Development Award in 2017, the National Innovation Award in 2018 and 2019, Future Tech Breakthrough Award 2019,and the Outstanding Elite Award, Chung Hwa Rotary Educational Foundation 2019–2020.