MARKOV CHAIN HIDDEN CONDITIONAL RANDOM FIELDS MODEL BASED PATTERN RECOGNITION METHOD

摘要

Provided is a method of recognizing patterns based on a hidden conditional random fields model to which full-Gaussian covariance has been applied. The method includes dividing a training input signal and outputting a frame sequence, extracting a feature vector from the frame sequence, calculating a parameter through a conditional random fields model to which Gaussian covariance has been applied using the feature vector, receiving, by the hidden conditional random fields model to which the parameter has been applied, a feature vector extracted from a test input signal measured for an actual pattern to infer a label indicating the actual pattern, and proposing a method of calculating gradient values for a conditional probability vector, a transition probability vector, a Gaussian mixture weight, a mean of Gaussian distributions, and covariance of the Gaussian distributions, as an analysis method.