Decisions about features

This paper describes the methods of statistical analysis used to classify letters in a feature‐based, speaker‐independent isolated letter recognition system. A hierarchical decision structure was implemented so that decisions at each node of the decision tree could be made on the basis of a small number of relevant features. For example, the 26 letters were first classified into vowel categories on the basis of first and second formant frequencies. The specific decisions, and the features used to make them, were selected by a clustering analysis of training data. At each decision node of the recognition system the test utterance was first analyzed using Fisher linear discriminant functions, with threshold weights individually set for each pairwise decision in order to minimize misclassifications. When a decision could not be made with certainty, classification was performed using a maximum likelihood procedure assuming multivariate Gaussian statistics. The sequential use of nonparametric and parametric discriminant functions produced superior classification to that obtained with either of the separate analyses. The overall system structure will be discussed in terms of practical tradeoffs between the number of features used at each decision node and the system's overall probability of error. [Work supported by NSF.]