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2010
IEEE

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

9 years 1 months ago
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.
Abhishek Kumar, John H. L. Hansen
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Abhishek Kumar, John H. L. Hansen
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