Faculty of Information Technology, BUT

Course details

Classification and recognition

KRD Acad. year 2017/2018 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

Czech

Completion

Examination

Time span

39 hrs lectures

Assessment points

100 exam

Department

Lecturer

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.

Generic learning outcomes and competences

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 kwnowledge 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.

Fundamental 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

  1. Estimation of parameters of Gaussian probability distribution by Maximum Likelihood (ML)
  2. Estimation of parameters of Gaussian Gaussian Mixture Model (GMM) by Expectation-Maximization (EM)
  3. Discriminative training, introduction, formulation of the objective function
  4. Discriminative training with the Maximum Mutual information (MMI) criterion
  5. Adaptation of GMM models- Maximum A-Posteriori (MAP), Maximum Likelihood Linear Regression (MLLR)
  6. Transforms of features for recognition - basis, Principal component analysis (PCA)
  7. Discriminative transforms of features - Linear Discriminant Analysis (LDA) and Heteroscedastic Linear Discriminant Analysis  (HLDA)
  8. Modeling of feature space using discriminative sub-spaces - factor analysis
  9. Kernel techniques, SVM
  10. Calibration and fusion of classifiers
  11. Applications in recognition of speech, video and text
  12. Student presentations I
  13. Student presentations II

Syllabus - others, projects and individual work of students

Progress assessment

Controlled instruction

Exam prerequisites

Course inclusion in study plans

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