11-751 Speech Recognition and Understanding
Instructor: Alex Waibel
MW 10:30am-11:50am, HBH 1511 and F 11:30am-12:50pm, HBH 1002
Enrollment Limit: 30

Description: The technology to allow humans to communicate by speech with machines or by which machines can understand when humans communicate with each other is rapidly maturing. This course provides an introduction to the theoretical tools as well as the experimental practice that has made the field what it is today. We will cover theoretical foundations, essential algorithms, major approaches, experimental strategies and current state-of-the-art systems and will introduce the participants to ongoing work in representation, algorithms and interface design. This course is suitable for graduate students with some background in computer science and electrical engineering, as well as for advanced undergrduates.

Prerequisites:
Sound mathematics background, knowledge of basic statistics, good computing skills. No prior experience with speech recognition is necessary. This course is primarily for graduate students in LTI, CS, Robotics, ECE, Psychology, or Computational Linguistics. Others by prior permission of instructor.

Recommended/Required Text:
Waibel and Lee, Readings in Speech Recognition, Morgan Kaufman Publishers, San Mateo, CA, 1990.

Rabiner and Juang, Fundamentals of Speech Recognition, Prentice Hall Signal Processing Series, Englewood Cliffs, NJ, 1993.

Evaluation Process:
Grading will be based on afinal exam, a course project and occasional lab and homework assignments. (Sharon, % are not listed.)

Topics to be Covered:

Speech
• Speech production and perception
• Digital Signal Processing for Speech Signals
Recognition Approaches
• Template-based recognition
• Hidden Markov modeling
• Neural nets
Search Techniques
Language Processing
• Modeling language
• Spoken language understanding
Speech System Design
• Robust recognition
• Speech interface design
• Multimodalinterfaces
• System examples, applications