Feature Compensation Incorporating Modeling Error Statistics

Abstract

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the error distribution of speech corruption model in the log spectral domain and represent the statistics as functions with respect to the signal-to-noise ratio. The proposed algorithm incorporates modeling error statistics into the interacting multiple model technique and shows a performance improvement over the AURORA2 speech recognition task.