Propagation of Uncertainty Through Multilayer Perceptrons for Robust Automatic Speech Recognition

Observation uncertainty techniques offer a way to dynamically compensate automatic speech recognizers to account for the information missing in real world scenarios. These techniques have been demonstrated to effectively be able to compensate multiple environment distortions and improve the integration of ASR systems with speech enhancement pre-processing through uncertainty propagation. Unfortunately observation uncertainty techniques rely on statistical methods and as such are limited to GMM-HMM architectures. In this paper we explore the application of observation uncertainty and uncertainty propagation techniques to multi-layer perceptrons (MLPs). We develop solutions for propagation through a generic MLP and exemplify potential gains with an large vocabulary robust ASR experiment on the AURORA4 database using an Hybrid MLP-HMM recognizer.