Methods of Pattern Recognition with Application to Voice
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Digital Signal Processing
Prerequisites: ECE 4270
Catalog DescriptionTheory and application of pattern recognition with a special
application section for automatic speech recognition and related
Student OutcomesIn the parentheses for each Student Outcome:
"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
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.
Review of probablilty with an emphasis on random vectors
Linear transformations, diagonalizations, rotations, projections
Clustering (unsupervised pattern recognition).
Sums of distances
Intraset distances (distortion measures)
MAP classification, Bayesian analysis
Minimum risk criteria, Neyman-Pearson criteria
Gaussian mixture densities, EM algorithm
Non-Gaussian: training of densities using basis functions
Linear discriminant functions
Single layer perceptron
Gradient descent algorithms
Nonlinear transformations prior to LDFs (potential functions).
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
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
Markov Processes, hidden and observed
Discrete HMM's, Recognition and Training
Continuous Observation HMM's
Connected Words: Level Building
Large Vocabulary Systems