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ECE Course Syllabus

ECE6273 Course Syllabus


Methods of Pattern Recognition with Application to Voice (3-0-3)

Technical Interest
Digital Signal Processing

ECE 4270


Catalog Description
Theory and application of pattern recognition with a special application section for automatic speech recognition and related signal processing.

Theodorous, Sergios and Koutroumbas, Konstantinos, Pattern Recognition (4th edition), Academic Press, 2008. ISBN 9781597492720 (required)

Indicators (SPIs)
SPIs are a subset of the abilities a student will be able to demonstrate upon successfully completing the course.

Outcome 1 (Students will demonstrate expertise in a subfield of study chosen from the fields of electrical engineering or computer engineering):
1.	Using theory learned in class the students should be able to display speech waveforms and images for visually inspecting key information and cues needed for pattern recognition. 

Outcome 2 (Students will demonstrate the ability to identify and formulate advanced problems and apply knowledge of mathematics and science to solve those problems):
1.	Using tools learned in class the students should be able to design feature extraction and pattern classification modules needed for putting together a pattern recognition system.

Outcome 3 (Students will demonstrate the ability to utilize current knowledge, technology, or techniques within their chosen subfield):
1.	Given a project at work, the students should be able to carry out literature survey, problem formulation, and experimental design by relating real-world pattern recognition problems to what they learn in class.

Topical Outline
Review of probablilty with an emphasis on random vectors
Linear transformations, diagonalizations, rotations, projections
Distance measures
Clustering (unsupervised pattern recognition).
        Interset distances
        Sums of distances
        Intraset distances (distortion measures)
        Performance measures
        Hierarchical clustering
Parametric Modeling
        MAP classification, Bayesian analysis
        Minimum risk criteria, Neyman-Pearson criteria
        Gaussian assumptions
        Gaussian mixture densities, EM algorithm
        Non-Gaussian: training of densities using basis functions
Linear discriminant functions
        Single layer perceptron
        Gradient descent algorithms
        Widrow-Hoff algorithm
        Nonlinear transformations prior to LDFs (potential functions).
Neural Networks
        Feedforward (MLPs)
        Back Propagation.
        Radial Basis function NNs (RBFs)
        Self-organizing feature maps
Data (Dimensionality) Reduction
Intro to sequence comparisons: time warps and stochastic grammars.
Intro to acoustic phonetics
Front ends (feature acquisition) for speech
Filter banks and LPC
Auditory models
Development of Mel-Cepstra from both a PR and
        DSP point of view (Karhunen-Loeve transformation)
Dynamic Time Warping (Deterministic  and Probabilistic)
Clustering for VQ-DTW, Training, Template Adaptation 
Discriminative Methods
Robust methods
Markov Processes, hidden and observed
Discrete HMM's, Recognition and Training
Continuous Observation HMM's
Semi-Markov Models
Model Adaptation 
Connected Words: Level Building
Large Vocabulary Systems 
Word Spotting 
Speaker ID