Statistical Inference of Missing Speech Data in the ICA Domain

We address the problem of speech estimation as statistical estimation with "missing" data in the independent component analysis (ICA) domain. Missing components are substituted by values drawn from "similar" data in a multi-faceted ICA representation of the complete data. The paper presents the algorithm for the inference of missing data in the case of a fixed pattern of missing data. We apply our approach to the problem of bandwidth extension, or where speech is degraded by a fixed filtering process and show the capability of the algorithm to reconstruct fine missing details of the original data with little artifacts. The evaluation is done using objective distortion measures on speech samples from the NTT database