Modeling uncertainty of data observation

Abstract

An approach is presented both theoretically and experimentally which overcomes a number of existing conceptual and performance problems in density estimation. The theoretical approach shows methods for incorporating or estimating uncertainties into speech recognition. In the maximum mutual information (MMI) and maximum likelihood (ML) case, precise formulae are given for estimation of densities for uncertainty variances small compared to the curvature of the posteriors. For implementation, the theoretical formulae are presented in such a way that the additional computation effort goes linearly with the number of densities. Experiments on car digits show relative improvements in word error rate of at most 4.8% relative. Uncertainty modelling is shown to help remedy effects of the sparse data problem in density estimation.