题目：Bayesian Nonparametrics for Speech and Language Processing
This talk surveys a series of Bayesian nonparametric (BNP) approaches to model selection and their inference procedures which are applied to build information systems including speech recognition, document classification, document summarization and document retrieval. Our goal is to design a flexible, scalable, hierarchical and robust topic models to meet the heterogeneous and nonstationary environments in the era of big data. Two recent works on BNP learning are introduced. One is the hierarchical Pitman-Yor-Dirichlet process for language modeling. The other is the hierarchical theme and topic modeling for document summarization.
Jen-Tzung Chien received his Ph.D. degree in electrical engineering from Tsing Hua University, Hsinchu, in 1997. During 1997-2012, he was with the Cheng Kung University, Tainan. Since 2012, he has been with the Department of Electrical and Computer Engineering, Chiao Tung University, Hsinchu. His research interests include machine learning, speech recognition, source separation and information retrieval.
Dr. Chien served as the associate editor of the IEEE Signal Processing Letters, in 2008-2011, the tutorial speaker of the ICASSP, in 2012 and 2015 and the INTERSPEECH, in 2013 and 2016 and is currently serving as an elected member of the IEEE Machine Learning for Signal Processing Technical Committee.