A kernel mean matching approach for environment mismatch compensation in speech recognition

The mismatch between training and test environmental conditions presents a challenge to speech recognition systems. In this paper, we investigate an approach for matching the distributions of training and test data in the feature space. This approach uses the property of reproducing kernel Hilbert space (RKHS) with a universal kernel for the task of distribution matching. The approach is unsupervised, requiring no transcripts of data for compensation, and can be employed either with explicit adaptation data or with live test data. The approach is evaluated on two real car environments - CU-Move and UTDrive. Relative improvements of between 10-25% are obtained for different experimental setups.