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

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