Robust Recognition of Noisy Speech Through Partial Imputation of Missing Data

Abstract

Two main categories of speech recognition robustness through missing data are spectral imputation and classifier modification. In this paper, we introduce a novel technique that could combine methods from these two categories while improving the accuracy of the combined methods. Methods in these two categories are rarely employed together due to their incompatible structures. Based on our previous work, we propose a technique to solve the problem of incompatibility. The technique is based on the idea of partial restoration of the log-spectrum. We decide to whether restore or estimate a possible range for the missing component. We also propose a method to more effectively employ dynamic features. The combined techniques are a classic spectral imputation method and our previously proposed classifier modification technique, namely spectral variance learning. The experiments show that the proposed technique is able to improve the accuracies of both combined techniques significantly, leading to improvements in recognition accuracy as high as nearly four percent on Aurora 2.0 data and more than two percent on a noisy version of TIMIT data.