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)
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). Centroids Interset distances Sums of distances Intraset distances (distortion measures) Algorithms Performance measures VQ 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
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