Noisy Speech Feature Estimation on the Aurora2 Database using a Switching Linear Dynamic Model

Abstract

This paper presents an approach to enhance speech feature estimation in the log spectral domain under additive noise environments. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, enforcing a state transition in the feature space and capturing the smooth time evolution of speech conditioned on the state sequence. Experimental results using the Aurora2 database show that the new SLDM approach can improve speech enhancement performance in terms of recognition accuracy.