Course details
Classification and Recognition
KRD Acad. year 2019/2020 Summer semester
Estimation of parameters Maximum Likelihood and Expectation-Maximization, formulation of the objective function of discriminative training, Maximum Mutual information (MMI) criterion, adaptation of GMM models, transforms of features for recognition, modeling of feature space using discriminative sub-spaces, factor analysis, kernel techniques, calibration and fusion of classifiers, applications in recognition of speech, video and text.
Guarantor
Language of instruction
Completion
Time span
- 39 hrs lectures
Assessment points
- 100 pts final exam
Department
Lecturer
Instructor
Subject specific learning outcomes and competences
The students will get acquainted with advanced classification and recognition techniques and learn how to apply basic methods in the fields of speech recognition, computer graphics and natural language processing.
The students will learn to solve general problems of classification and recognition.
Learning objectives
To understand advanced classification and recognition techniques and to learn how to apply the algorithms and methods to problems in speech recognition, computer graphics and natural language processing. To get acquainted with discriminative training and building hybrid systems.
Prerequisite knowledge and skills
Basic knowledge of statistics, probability theory, mathematical analysis and algebra.
Study literature
- Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
- Fukunaga, K. Statistical pattern recognition, Morgan Kaufmann, 1990, ISBN 0-122-69851-7.
Syllabus of lectures
- Estimation of parameters of Gaussian probability distribution by Maximum Likelihood (ML)
- Estimation of parameters of Gaussian Gaussian Mixture Model (GMM) by Expectation-Maximization (EM)
- Discriminative training, introduction, formulation of the objective function
- Discriminative training with the Maximum Mutual information (MMI) criterion
- Adaptation of GMM models- Maximum A-Posteriori (MAP), Maximum Likelihood Linear Regression (MLLR)
- Transforms of features for recognition - basis, Principal component analysis (PCA)
- Discriminative transforms of features - Linear Discriminant Analysis (LDA) and Heteroscedastic Linear Discriminant Analysis (HLDA)
- Modeling of feature space using discriminative sub-spaces - factor analysis
- Kernel techniques, SVM
- Calibration and fusion of classifiers
- Applications in recognition of speech, video and text
- Student presentations I
- Student presentations II
Course inclusion in study plans