BOUNDED CONDITIONAL MEAN IMPUTATION WITH GAUSSIAN MIXTURE MODELS: A RECONSTRUCTION APPROACH TO PARTLY OCCLUDED FEATURES

In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partly occluded by a superimposed interference, as then the noisy observation poses an upper bound. Unfortunately, the occurring integrals are not analytic. Hence an approximate solution has to be used. In the experimental section we apply the bounded approach to the reconstruction of partly occluded speech spectra and demonstrate its superiority over the unbounded case with respect to automatic speech recognition performance.