6.345 Automatic Speech Recognition
Spring 2019
Instructor: James R Glass
TA: Wei-Ning Hsu
Lecture: TR11-12.30 (37-212)
Information:
Introduces the rapidly developing fields of automatic speech recognition and spoken language processing. Topics include acoustic theory of speech production, acoustic-phonetics, signal representation, acoustic and language modeling, search, hidden Markov modeling, neural networks models, adaptation, and other related speech processing topics. Lecture material intersperses theory with practice. Includes problem sets, laboratory exercises, and opened-ended term project.
There will be two 90 minute lectures per week, along with office hours. Lecture material will be front-loaded into the first half of the class, so that students are prepared for the term project which will start after spring break. There will be invited guest lectures on related speech and language topics in the latter half of the class.
There will be five assignments interspersed over the first half of the class as well, which include problems, a laboratory. The assignments will be closely linked with lecture material. The laboratories will involve speech recognition experiments that can be performed on the Holyoke compute cluster. All material will be made available on the MIT Stellar course website.
Each of the five assignments will count 10% towards the final
grade. The final term project will count towards 50% of the final
grade.
Announcements
No announcements
