Automatic Speech Recognition 2016

Automatic Speech Recognition 2016

Course Description

The aim of the course is to make students familiar to the fundamentals of the speech recognition technology. We will learn how to extract informative features from speech, how to model speech dynamics with hidden Markov models, and how to build language models corresponding to the task domain. A brief historical overview of the speech recognition methods as well as the future trends will be given. During the practical part of the course students will be proposed to develop a basic speech recognizer using modern open-source software. 

Course tools

Python, Java, CMU Sphinx


General knowledge on machine learning, basic programming skills, neural networks (optional).


Mr. Dmytro Prylipko
Machine Learning Engineer & Software Developer

Affiliation: BuddyGuard UG, Otto-von-Guericke University Magdeburg, Germany

Fields of interests: Speech processing, computer vision.


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