Feature compensation based on switching linear dynamic model and soft decision

Abstract

In this paper, we present a new approach to feature compensation for robust speech recognition in noisy environments. We employ the switching linear dynamic model (SLDM) as a parametric model for the clean speech distribution, which enables us to utilize temporal correlations in speech signals. Both the background noise and clean speech components are simultaneously estimated by means of the interacting multiple model (IMM) algorithm. Moreover, we combine the SLDM algorithm with the spectral subtraction (SS) approach based on a soft decision. Performance of the presented compensation technique is evaluated through the experiments on AURORA 2 database.