Model-Based Wiener filter for noise robust speech recognition

In this paper, we propose a new approach for noise robust speech recognition, which integrates signal-processing-based spectral enhancement and statistical-model-based compensation. The proposed method, Model-Based Wiener filter (MBW), takes three steps to estimate clean speech signals from noisy speech signals, which are corrupted by various kinds of additive background noise. The first step is the well-known spectral subtraction (SS). Since the SS averagely subtracts noise components, the estimated speech signals often include distortion. In the second step, the distortion caused by SS is reduced using the minimum mean square error estimation for a Gaussian mixture model representing pre-trained knowledge of speech. In the final step, the Wiener filtering is performed with the decision-directed method. Experiments are conducted using the Aurora2-J (Japanese digit string) database. The results show that the proposed method performs as well as the ETSI advanced front-end in average and the variation range of the recognition accuracy according to the kind of noise is about one third, which demonstrates the robustness of the proposed method.