Adding Noise to Improve Noise Robustness in Speech Recognition

In this work we explore a technique for increasing recognition accuracy on speech affected by corrupting noise of an undetermined nature, by the addition of a known and well-behaved noise (masking noise). The same type of noise used for masking is added to the training data, thus reducing the gap between training and test conditions, independent of the type of corrupting noise, or whether it is stationary or not. While still in an early development stage, the new approach shows consistent improvements in accuracy and robustness for a variety of conditions, where no use is made of a-priori knowledge of the corrupting noise. The approach is shown to be of particular interest to the case of cross-talk corrupting noise, a complicated situation in speech recognition for which the relative gain with the proposed approach is over 24%.