Switching linear dynamic models for noise robust in-car speech recognition

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise in the interior of a car. We compare two different Kalman filtering approaches which attempt to improve noise robustness: Switching Linear Dynamic Models (SLDM) and Autoregressive Switching Linear Dynamical Systems (AR-SLDS). Unlike previous works which are restricted on considering white noise, we evaluate the modeling concepts in a noisy speech recognition task where also colored noise produced through different driving conditions and car types is taken into account. Thereby we demonstrate that speech enhancement based on Kalman filtering prevails over all standard de-noising techniques considered herein, such as Wiener filtering, Histogram Equalization, and Unsupervised Spectral Subtraction.