TALK    Bayesian Group Sparse Learning

Date released: January 28, 2013

  •  TALK    Bayesian Group Sparse Learning
  • Date & Time:

    Monday, January 28, 2013; 11:00 AM

  • Abstract:

    Bayesian learning provides attractive tools to model, analyze, search, recognize and understand real-world data. In this talk, I will introduce a new Bayesian group sparse learning and its application on speech recognition and signal separation. First of all, I present the group sparse hidden Markov models (GS-HMMs) where a sequence of acoustic features is driven by Markov chain and each feature vector is represented by two groups of basis vectors. The features across states and within states are represented accordingly. The sparse prior is imposed by introducing the Laplacian scale mixture (LSM) distribution. The robustness of speech recognition is illustrated. On the other hand, the LSM distribution is also incorporated into Bayesian group sparse learning based on the nonnegative matrix factorization (NMF). This approach is developed to estimate the reconstructed rhythmic and harmonic music signals from single-channel source signal. The Monte Carlo procedure is presented to infer two groups of parameters. The future work of Bayesian learning shall be discussed.

  • Speaker:

    Prof. Jen-Tzung Chien
    National Chiao Tung University, Taiwan

    Jen-Tzung Chien received his Ph.D. degree in electrical engineering from National Tsing Hua University, Hsinchu, Taiwan, in 1997. During 1997-2012, he was with the National Cheng Kung University, Tainan, Taiwan. Since 2012, he has been with the Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu. He held the Visiting Researcher positions at the Panasonic Technologies Inc., Santa Barbara, CA, the Tokyo Institute of Technology, Tokyo, Japan, the Georgia Institute of Technology, Atlanta, GA, the Microsoft Research Asia, Beijing, China, and the IBM T. J. Watson Research Center, Yorktown Heights, NY. His research interests include machine learning, speech recognition, blind source separation, face recognition and information retrieval. Dr. Chien is a senior member of the IEEE Signal Processing Society. He served as the associate editor of the IEEE Signal Processing Letters, in 2008-2011, and the tutorial speaker of the ICASSP, in 2012. He is appointed as the APSIPA Distinguished Lecturer for 2012-2013. He was a co-recipient of the Best Paper Award of the IEEE Automatic Speech Recognition and Understanding Workshop in 2011.

  • Research Area:

    Speech & Audio