Speech Feature Estimation Under the Presence of Noise with a Switching Linear Dynamic Model

This paper presents an approach to enhance speech feature estimation in the log spectral domain under noisy environments. A higher-order switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, which enforces a state transition in the feature space and captures the smooth time evolution of speech conditioned on the state sequence. The clean speech components are estimated by means of an Interacting Multiple Model (IMM) algorithm. Our experimental results show that increasing the order of the linear dynamic model in the SLDM and the introduction of transition probabilities among the linear dynamic models can improve the performance of SLDM systems in feature compensation for robust speech recognition.