Noise Robust LVCSR feature extraction based on stabilized weighted linear prediction

In this paper, we evaluate a recently proposed spectral envelope estimation method, stabilized weighted linear prediction (SWLP), in the feature extraction stage of a large vocabulary continuous speech recognizer (LVCSR) system. Using speech recorded in real-world noisy environments, we compare recognition error rates obtained with SWLP to those given by the conventional spectrum estimation methods in feature extraction. We use large vocabulary speech that is simultaneously recorded using a headset, lavalier and a fixed medium-distance microphone. When the recognizer is trained using a multicondition training set the results do not differ significantly. However, when the models are trained with clean speech and evaluated in noisy environments, the SWLP models perform significantly better than the conventional MFCC in all environments and in all recording settings.