This course gives a computer science presentation of automatic speech
recognition, the problem of transcribing accurately spoken
utterances. The description includes the essential algorithms for
creating large-scale speech recognition systems. The algorithms and
techniques presented are now used in most research and industrial
Many of the learning and search algorithms and techniques currently
used in natural language processing, computational biology, and other
areas of application of machine learning were originally designed for
tackling speech recognition problems. Speech recognition continues to
feed computer science with challenging problems, in particular because
of the size of the learning and search problems it generates.
The objective of the course is thus not just to familiarize students with particular algorithms used in speech recognition, but rather use that as a basis to explore general text and speech and machine learning algorithms relevant to a variety of other areas in computer science. The course will make use of several software libraries and will study recent research and publications in this area.
Here are some of the topics covered by this course.
Reading and Software Material
There is no single textbook covering the material presented in this course. The following are some recommended books or papers. An extensive list of recommended papers for further reading is provided in the lecture slides.
Location and Time
Room 102 Warren Weaver Hall,
251 Mercer Street.
Mondays 5:00 PM - 6:50 PM.
Familiarity with basics in linear algebra, probability, and analysis of algorithms. No specific knowledge about signal processing or other engineering material is required.
Interest in theoretical and applied machine learning or prior acquaintance with machine learning concepts as presented or discussed in "Foundations of Machine Learning" or the Ph.D. seminar in machine learning, or with natural language processing will be helpful.
3 assignments and a project. The final grade is an average of the assignment and project grades. The standard high level of integrity is expected from all students, as with all CS courses.