A New Method to Combine Probability Estimates from Pairwise Binary Classifiers.
Ondrej Šuch, Štefan Beňuš, Andrea Tinajová · ITAT · 2015
Estimating class membership probabilities is an important step in many automated speech recognition systems. Since binary classifiers are usually easier to train, one common approach to this problem is to construct pairwise binary classifiers. Pairwise models yield an overdetermined system of equations for the class membership probabilities. Motivated by probabilistic arguments we propose a new way for estimating individual class membership probabilities, which reduces to solving a linear system of equations. A solution of this system is obtained by finding the unique non-zero eigenvector of total probability one, corresponding to eigenvalue one of a positive Markov matrix. This is a property shared by another algorithm previously proposed by Wu, Lin, and Weng. We compare properties of these methods in two settings: a theoretical three-way classification problem, and via classification of English monophthongs from TIMIT corpus.