A pattern classification algorithm for the voiced/Unvoiced decision
L. Siegel, Ken Steiglitz · 2005
An algorithm for making the voiced/unvoiced decision in speech analysis is presented. Three features (LPC normalized minimum error, ratio of energy content at high to low frequencies, and input RMS) define a three-dimensional space in which the decision making process is viewed as a pattern classification problem. This is formulated as a linear program which runs on a training set to find a hyperplane dividing the V/UV regions if they are separable, or minimizing the distance by which misclassification occurs if they are not. A procedure is given for selecting the features and constructing the training set.