Objective evaluation of the discriminant power of features in an HMM-based word recognition system
Objective evaluation of the discriminant power of features in an HMM-based word recognition system
El-Yacoubi, A. and Gilloux, M. and Sabourin, R. and Suen, C. Y.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1997
Abstract : This paper describes an elegant method for evaluating the discriminant power of features in the framework of an HMM-based word recognition system. This method employs statistical indicators, entropy and perplexity, to quantify the capability of each feature to discriminate between classes without resorting to the result of the recognition phase. The HMMs and the Viterbi algorithm are used as powerful tools to automatically deduce the probabilities required to compute the abovementioned quantities.
