Handling Time-Derivative Features in a Missing Data Framework for Robust Automatic Speech Recognition

We present a novel approach to handling dynamic (time derivative or delta) features for automatic speech recognition using a HMM/GMM-architecture and based on missing data techniques for noise robustness. The static and the dynamic features are imputed in the observations based on an acoustic model expressed in a domain that is a linear transform of the log-spectra and taking bounds into account. The reliability masks of the dynamic features are ternary. We describe a method for computing oracle masks for dynamic features. We also propose a simple method to derive dynamic masks from the reliability mask of the static features. We find that using bounds in the imputation is advantageous, both for oracle masks and for masks derived from the noisy observations