Observation uncertainty measures for sparse imputation

Missing data imputation estimates the clean speech features for automatic speech recognition in noisy environments. The estimates are usually considered equally reliable while in reality, the estimation accuracy varies from feature to feature. In this work, we propose uncertainty measures to characterise the expected accuracy of a sparse imputation (SI) based missing data method. In experiments on noisy large vocabulary speech data, using observation uncertainties derived from the proposed measures improved the speech recognition performance on features estimated with SI. Relative error reductions up to 15 % compared to the baseline system using SI without uncertainties were achieved with the best measures.