Roger Hsiao's homepage

Roger W. Hsiao

Roger Hsiao received his M.S. degree from the Language Technologies Institute (LTI) of Carnegie Mellon University in 2007, and his B. Eng. and M. Phil. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2002 and 2004. From August 2007, He became a PhD candidate of LTI under the supervision of Prof Tanja Schultz. His research interests include speech recognition, machine learning and pattern recognition. He received a honorable mention award in 2009 LTI Student Research Symposium. In 2002, He represented HKUST in the ACM International Collegiate Programming Contest and became a coach of HKUST programming team in 2003. He received a merit award in the 5th ACM Postgraduate Research Day organized by ACM Hong Kong Chapter in 2004 for his research on discriminative training.

Roger Hsiao, Mark Fuhs, Yik-Cheung Tam, Qin Jin, Ian Lane and Tanja Schultz. The CMU-InterACT Mandarin Transcription System for GALE. The GALE book, to appear 2009
Roger Hsiao and Tanja Schultz. Generalized Discriminative Feature Transformation for Speech Recognition. Interspeech 2009, CMU LTI SRS 2009 honorable mention. (draft pdf)
Roger Hsiao, Yik-Cheung Tam and Tanja Schultz. Generalized Baum-Welch Algorithm for Discriminative Training on Large Vocabulary Continuous Speech Recognition System. ICASSP 2009. (draft pdf)
Brian Mak and Roger Hsiao. Kernel Eigenspace-based MLLR Adaptation.The IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 3 March, 2007. (draft pdf)
Brian Mak, James Kwok, Simon Ho and Roger Hsiao. Embedded Kernel Eigenvoice Speaker Adaptation and Its Implication to Reference Speaker Weighting. The IEEE Transactions on Speech and Audio Processing, vol. 14, no. 4, July 2006 . (draft pdf)
Roger Hsiao and Brian Mak. Kernel Eigenspace-based MLLR Adaptation using Multiple Regression Classes. ICASSP2005. (draft pdf)

LTI 11751: Speech Recognition and Understanding (Fall 2009)
- Introduction to Speech Recognition
- Covered acoustic and language modeling.
- Applications of speech technologies.
Comp201: Java Programming (Fall 2003)

Introduction to Java programming.
Fundamentals include language syntax, object oriented programming, inheritance, polymorphism, exception handling, multithreading.
Standard libraries for input/output, graphics programming, built-in data structures.
Application programming interface and foundation class library.

Comp300Y: Introduction to Machine Learning (Summer 2003)
- Introduction to machine learning.
- Covered decision tree learning, artificial neural networks, kernel methods, Bayesian learning, unsupervised learning, and reinforcement learning.
Comp201: Java Programming (Spring 2003)
Comp251: Principles of Programming Languages (Fall 2002)
- Comparative studies of programming languages, programming language concepts and constructs.
- Non-imperative programming paradigms: object-oriented, functional, logic, concurrent programming.
- Basic concepts of program translation and interpretation.
- Storage allocation and run-time organization.

Course description comes from here.