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Course Description
Hidden Markov Models (HMMs) are widely used in today's speech
recognition systems. This course is an introduction to the theory and
practice of speech
recognition using HMMs. Topics include dynamic time warping,
Markov Models and Hidden Markov Models (discrete, semi-continuous, and
continuous), vector quantization, Gaussian Mixture Models, the Viterbi
search algorithm, the forward-backward training algorithm, language
modeling, and speech-specific adaptations of HMMs. The course is
focused on understanding these fundamental technologies and developing
the main components of speech recognition systems. Students can expect
to come away from the
course with an ability to write programs for the training and execution
of simple HMM systems, and to know how to extend these systems to more
complex cases. Prerequisite: C
programming experience.
For the latest version of this course, please go to http://www.cslu.ogi.edu/people/hosom/cs552
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