Duraction: 8 h
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.
Pre-requisites: general knowledge on machine learning, neural networks (optional).
Methods: Machine Learning & Pattern Recognition -> Supervised Learning:
- Hidden Markov models;
- Gaussian mixture models;
- Neural networks;
- Language modeling techniques: grammars (JSGF), N-grams.
Mr. Dmytro Prylipko
Place of employment: Otto-von-Guericke University Magdeburg, Germany
Spheres of research: affect recognition from speech, modeling non-linguistics vocalizations in spontaneous speech, recognition of spontaneous speech, spoken dialog systems design
Phone: +49 176 325 44 562