Noise adaptive front-end normalization based on Vector Taylor Series for Deep Neural Networks in robust speech recognition

Deep Neural Networks (DNNs) have been successfully applied to various speech tasks during recent years. In this paper, we investigate the use of DNNs for noise-robust speech recognition and demonstrate their superior capabilities of modeling acoustic variations over the conventional Gaussian Mixture Models (GMMs). We then propose to compensate the normalization front-end of the DNNs using the GMM-based Vector Taylor Series (VTS) model compensation technique, which has been successfully applied in the GMM-based ASR systems to handle noisy speech. To fully benefit from both the powerful modeling capability of the DNN and the effective noise compensation of the VTS, an adaptive training algorithm is further developed. The preliminary experimental results on the AURORA 2 task have demonstrated the effectiveness of our approach. The adaptively trained system has been shown to outperform the GMM-based VTS adaptive training by relatively 18.8% using the MFCC features and 21.9% using the FBank features.