Robust Distributed Speech Recognition Using Histogram Equalization And Correlation Information

In this paper, we propose a noise compensation method for robust speech recognition in DSR (Distributed Speech Recognition) systems based on histogram equalization and correlation information. The objective of this method is to exploit the correlation between components of the feature vector and the temporal correlation between consecutive frames of each component. The recognition experiments, including results in the Aurora 2, Aurora 3-Spanish and Aurora 3-Italian databases, demonstrate that the use of this correlation information increases the recognition accuracy.